A PSO-Based Classification Rule Mining Algorithm
基于Super_SBM模型的低碳经济发展绩效评价研究_周泽炯
2013， 35 （12） ： 2457-2466
Vol.35， No.12 Dec.， 2013
Resources Science
基于 Super-SBM 模型的低碳经济发展绩效评价研究
31投入指标选取一国或地区要实现经济发展能源投入必不可少而要实现低碳技术开发与进步又离不开研发投入此外产业结构特征和城镇化水平又从不同侧面对碳排放水平与能源消费结构产生影响2223此在国内外学者的相关研究基础上本文从能源研发产业结构城镇化水平等角度分别选取相应指标以衡量中原经济区低碳经济发投入与多个产出的决 策单元之间的相对效率。由于具有不需设定任何 权重、 不需提前设定生产前沿函数具体形式以及特 别适用于多投入多产出模型效率评价等诸多优点， DEA 在工业部门、 金融机构、 企业技术效率等经济 社会和管理领域等得到广泛应用。传统 DEA 模型 有 CCR 模 型（Chames， Cooper 和 Rhodes， 1978）和 BCC 模型 （Banker， Chames 和 Cooper， 1984） 两种， 前
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classification
classificationClassification is a fundamental task in machine learning and data analysis. It involves categorizing data into predefined classes or categories based on their features or characteristics. The goal of classification is to build a model that can accurately predict the class of new, unseen instances.In this document, we will explore the concept of classification, different types of classification algorithms, and their applications in various domains. We will also discuss the process of building and evaluating a classification model.I. Introduction to ClassificationA. Definition and Importance of ClassificationClassification is the process of assigning predefined labels or classes to instances based on their relevant features. It plays a vital role in numerous fields, including finance, healthcare, marketing, and customer service. By classifying data, organizations can make informed decisions, automate processes, and enhance efficiency.B. Types of Classification Problems1. Binary Classification: In binary classification, instances are classified into one of two classes. For example, spam detection, fraud detection, and sentiment analysis are binary classification problems.2. Multi-class Classification: In multi-class classification, instances are classified into more than two classes. Examples of multi-class classification problems include document categorization, image recognition, and disease diagnosis.II. Classification AlgorithmsA. Decision TreesDecision trees are widely used for classification tasks. They provide a clear and interpretable way to classify instances by creating a tree-like model. Decision trees use a set of rules based on features to make decisions, leading down different branches until a leaf node (class label) is reached. Some popular decision tree algorithms include C4.5, CART, and Random Forest.B. Naive BayesNaive Bayes is a probabilistic classification algorithm based on Bayes' theorem. It assumes that the features are statistically independent of each other, despite the simplifying assumption, which often doesn't hold in the realworld. Naive Bayes is known for its simplicity and efficiency and works well in text classification and spam filtering.C. Support Vector MachinesSupport Vector Machines (SVMs) are powerful classification algorithms that find the optimal hyperplane in high-dimensional space to separate instances into different classes. SVMs are good at dealing with linear and non-linear classification problems. They have applications in image recognition, hand-written digit recognition, and text categorization.D. K-Nearest Neighbors (KNN)K-Nearest Neighbors is a simple yet effective classification algorithm. It classifies an instance based on its k nearest neighbors in the training set. KNN is a non-parametric algorithm, meaning it does not assume any specific distribution of the data. It has applications in recommendation systems and pattern recognition.E. Artificial Neural Networks (ANN)Artificial Neural Networks are inspired by the biological structure of the human brain. They consist of interconnected nodes (neurons) organized in layers. ANN algorithms, such asMultilayer Perceptron and Convolutional Neural Networks, have achieved remarkable success in various classification tasks, including image recognition, speech recognition, and natural language processing.III. Building a Classification ModelA. Data PreprocessingBefore implementing a classification algorithm, data preprocessing is necessary. This step involves cleaning the data, handling missing values, and encoding categorical variables. It may also include feature scaling and dimensionality reduction techniques like Principal Component Analysis (PCA).B. Training and TestingTo build a classification model, a labeled dataset is divided into a training set and a testing set. The training set is used to fit the model on the data, while the testing set is used to evaluate the performance of the model. Cross-validation techniques like k-fold cross-validation can be used to obtain more accurate estimates of the model's performance.C. Evaluation MetricsSeveral metrics can be used to evaluate the performance of a classification model. Accuracy, precision, recall, and F1-score are commonly used metrics. Additionally, ROC curves and AUC (Area Under Curve) can assess the model's performance across different probability thresholds.IV. Applications of ClassificationA. Spam DetectionClassification algorithms can be used to detect spam emails accurately. By training a model on a dataset of labeled spam and non-spam emails, it can learn to classify incoming emails as either spam or legitimate.B. Fraud DetectionClassification algorithms are essential in fraud detection systems. By analyzing features such as account activity, transaction patterns, and user behavior, a model can identify potentially fraudulent transactions or activities.C. Disease DiagnosisClassification algorithms can assist in disease diagnosis by analyzing patient data, including symptoms, medical history, and test results. By comparing the patient's data againsthistorical data, the model can predict the likelihood of a specific disease.D. Image RecognitionClassification algorithms, particularly deep learning algorithms like Convolutional Neural Networks (CNNs), have revolutionized image recognition tasks. They can accurately identify objects or scenes in images, enabling applications like facial recognition and autonomous driving.V. ConclusionClassification is a vital task in machine learning and data analysis. It enables us to categorize instances into different classes based on their features. By understanding different classification algorithms and their applications, organizations can make better decisions, automate processes, and gain valuable insights from their data.。
Data Mining：Concepts and Techniques
Types of Outliers (I)
Three kinds: global, contextual and collective outliers Global Outlier Global outlier (or point anomaly) Object is Og if it significantly deviates from the rest of the data set Ex. Intrusion detection in computer networks Issue: Find an appropriate measurement of deviation Contextual outlier (or conditional outlier) Object is Oc if it deviates significantly based on a selected context o Ex. 80 F in Urbana: outlier? (depending on summer or winter?) Attributes of data objects should be divided into two groups Contextual attributes: defines the context, e.g., time & location Behavioral attributes: characteristics of the object, used in outlier evaluation, e.g., temperature Can be viewed as a generalization of local outliers—whose density significantly deviates from its local area Issue: How to define or formulate meaningful context?
焙烧_酸浸_氰化法从复杂金精矿中回收金银铜_吴在玖
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然后装入 1000 mL 烧杯中 ， 按照液固体积质量比 （ 指 溶液每毫升液体中所含固体质量的克数 ， 下同 ）5∶1 加 入 1 moL/L 的稀硫酸溶液 ， 用 DF-1 型水浴锅控制浸 出 温 度 50 ℃ ， 机 械 搅 拌 浸 出 4 h ， 酸 浸 完 成 后 用
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4FeS 2+11O 2=2Fe 2O3+8SO 2 3FeS+5O 2=Fe 3O4+3SO 2 2FeS+7/2O 2=Fe 2O3+2SO 2
第 4 卷 第 2 期 2 0 13 年 4 月
有色金属科学与工程
Nonferrous Metals Science and Engineering
Vol.4， No.2 Apr . 2 0 13
文章编号： 1674-9669 （2013 ） 02-0025-05
焙烧 - 酸浸 - 氰化法从复杂金精矿中回收金银铜
表1
化学成分 含量 · Au /(g t-1)
根据试验要求加入相应种类和数量的添加剂 ， 加入 蒸馏水搅匀 ， 在电炉上蒸干 ， 待马弗炉达到预定实验 温度后 ， 置于马弗炉中焙烧 ， 焙 烧 结 束 后 ， 将 焙 砂 从 马弗炉中取出置于空气中冷却 .
压气机叶片一次加工合格率预测
压气机叶片一次加工合格率预测张 旭1，童一飞2*，胡骥川2（1.中国航发南方工业有限公司，湖南株洲 412002； 2.南京理工大学机械工程学院，江苏南京 210094）摘要：压气机叶片被广泛用于航空、能源等领域的气体压缩设备中，也被应用于农业装备中，以提高零部件的加工效率和质量，提高整机的可靠性和耐用性。
因此，其设计和加工的精度要求较高。
开展压气机叶片一次加工合格率预测技术研究，提出了PSO-BP预测模型，提高了网络的全局搜索能力以避免局部最优解，从而提升预测的准确度。
实验结果表明，PSO-BP模型的预测精度明显高于传统BP神经网络模型，预测的最大误差百分比为1.24%，平均误差百分比为0.24%，预测准确度达到96.67%。
关键词：压气机叶片；一次合格率；合格率预测；PSO-BP模型0 引言压气机叶片通常用于航空、能源等领域的气体压缩设备中，也应用于农业装备中，以提高零部件的加工效率和质量[1]，提高整机的可靠性和耐用性。
作为航空发动机的核心部件，叶片的质量很大程度上决定了发动机的性能，因此压气机叶片的质量尤为重要。
一次加工合格指的是压气机叶片柔性加工单元完成对叶片的加工之后未经过返工返修，第一次检验就能合格的压气机叶片。
而一次加工合格率指的是一次加工合格的压气机叶片占加工单元产出的比率。
本文以F型号叶片为例，对压气机叶片柔性加工单元所产出叶片的一次加工合格率进行预测，根据预测结果采取相应的预防性措施，减小压气机叶片加工单元产出叶片的品质出现重大问题的概率。
目前，产品质量合格率预测方法主要分为传统质量预测方法和人工智能方法2个大类。
传统的质量预测方法主要是基于统计过程控制的方法，人工智能方法的典型代表则是用神经网络预测产品合格率。
在人工智能方法预测产品合格率预测方面，Apriori 算法和FP-Growth算法是2种关联性规则分析的经典算法。
为了解决Apriori算法运行效率不高的缺点，Toivonen H[2] 探究得出以采样思想算法为基础，分析和阐述数据之间的关联性规则，从而实现算法运行的并行化。
高职《生物制药技术》课程与国家职业标准对接的“三个结合”
第49卷第2期2021年1月广㊀州㊀化㊀工Guangzhou Chemical IndustryVol.49No.2Jan.2021高职‘生物制药技术“课程与国家职业标准对接的 三个结合 ∗谢承佳,陈秀清,郭双华(扬州工业职业技术学院,江苏㊀扬州㊀225127)摘㊀要:基于高职院校培养高素质技术技能型人才的定位和目标,高职教育人才培养有必要在学科体系内在逻辑的基础上达成课程标准与国家职业标准的融通㊂本文以药品生产技术专业核心课程‘生物制药技术“课程为例,在实践研究的基础上提出了高等职业教育课程与国家职业标准对接需做到三个结合:教学模块与职业功能结合㊁教学项目与岗位工作任务结合㊁教学考核与职业技能鉴定结合,以最大限度地实现学校人才培养与企业人才需求的无缝对接㊂关键词:职业标准;课程标准;技能鉴定㊀中图分类号:G710㊀文献标志码:A文章编号:1001-9677(2021)02-0123-03㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀∗基金项目:扬州工业职业技术学院2018年校级教改课题 基于 一城六区 制药产业发展的高职院校‘生物制药技术“课程标准与国家职业标准对接研究 (课题编号:2018XJJG19);扬州工业职业技术学院2016年校级教育教改课题 能力本位视阈下提升高职院校教师校本课程开发能力的研究 (课题编号:2016XJJG07)㊂第一作者:谢承佳(1982-),女,副教授,主要研究方向为教育信息化㊁教学设计等㊂Three Combinations of Biopharmaceutical Technology Course in Higher Vocational Education and National Vocational Standards ∗XIE Cheng -jia ,CHEN Xiu -qing ,GUO Shuang -hua(Yangzhou Polytechnic Institute,Jiangsu Yangzhou 225127,China)Abstract :Based on the positioning and goal of cultivating high -quality skilled talents in higher vocational colleges,it was necessary to achieve the integration of curriculum standards and national vocational standards based on the discipline system.Taking the course of Biopharmaceutical Technology as an example,on the basis of practical research,it proposed that in order to maximize the seamless connection between school personnel training and corporate talent needs,the integration of higher vocational education courses and national vocational standards needed to be combined in three ways:the combination of teaching modules and professional functions,the combination of teaching projects and job tasks,and the combination of teaching assessment and vocational skill appraisal.Key words :vocational standards;curriculum standards;skill identification‘国家中长期教育改革和发展规划纲要(2010-2020年)“中指出 加快建立健全政府主导㊁行业指导㊁企业参与的办学机制,推动职业教育适应经济发展方式转变和产业结构调整要求,培养大批现代化建设需要的高素质劳动者和技能型人才 [1]㊂高职院校培养的是高素质技术技能型人才,是以培养岗位技能为核心,但是,我国高等职业教育发展历史较短,职业教育培养的毕业生具备的职业素养与企业需求的职业素养有一定的差异㊂基于这样的现状,同时在长期的教学实践中,我们认识到有必要在学科体系内在逻辑的基础上达成课程标准与国家职业标准的融通㊂国家职业标准是劳动技能的衡量准则,是对从业人员在某一专业领域的职业素质最基本要求,课程标准与国家职业标准的融通,可以使更多的受教育者和培训对象的职业技能与就业岗位相适应,最大限度的实现学校人才培养与企业人才需求的对接㊂在这样的基础上,本文探讨了药品生产技术专业‘生物制药技术“课程内容与职业标准对接的问题研究,希望能指导一线教学,提高人才培养质量㊂1㊀课程概况1.1㊀专业概况在全国范围内,江苏省是医药大省㊂截至2019年底,省内正规的制药企业有超过500家[2]㊂在这样的区域经济发展背景下,我院化学工程学院在2006首次开办了化学制药技术专业,目前已经过十多年的发展㊂2016年,根据国家的相关文件,专业名称变更为药品生产技术专业㊂在本专业开办之初,秉承 市场为导向,以就业为目的,以能力为本位 的人才培养观,对制药产业及企业进行了充分调研,确定主要岗位群㊂目前,根据调研结果,药品生产技术专业主要有六大主要岗位群,包括化学药品生产岗位㊁生物药物生产岗位㊁药物制剂生产岗位㊁化工生产岗位㊁药品经营与124㊀广㊀州㊀化㊀工2021年1月管理岗位㊁药品质量控制岗位㊂对不同的岗位分析对应的工作任务,从而提炼职业能力要求㊂例如,生物药物生产岗位对应的素质能力要求主要包括 掌握生物药物生产工艺流程及环境划分㊁设备操作及质量控制等相关知识,具备生物制药的基本理论知识和岗位操作技能;具有药物制剂制备与设备维护保养能力 ,而药物制剂生产岗位则需要员工能够 掌握典型剂型的生产工艺流程,具备典型制剂制备及质量控制的操作技能;熟悉常用制剂设备的基本操作,具有常用制剂设备使用与维护能力;懂得常用仪器的使用方法;有解决药物制剂制备过程中一般性技术问题的能力 [3]㊂根据各个岗位提炼的职业能力要求,确定了学习领域,设计了 基于工作过程系统化导向 的课程体系,主要由文化课㊁专业课和拓展课三大课程模块构成㊂其中,专业课又包括专业平台课,专业模块课和综合实践课,分别对应职业通用能力㊁专项能力和综合能力㊂‘生物制药技术“课程就属于专业模块课程,旨在提高学生的职业专项能力,主要对应岗位为生物药物生产岗位㊂1.2㊀‘生物制药技术“课程概况作为药品生产技术的专业核心课程,‘生物制药技术“课程教学的主要任务是使学生熟悉生物技术的发展与基本概念㊁掌握生物制药技术的操作与相关应用㊂其前导课程为‘微生物技术及应用“‘制药过程及设备选择与操作“㊂后续课程为‘药物制剂操控“‘药物分离与纯化“㊂在‘生物制药技术“课程教授的十多年时间中,随着区域经济发展,授课团队越发感觉到课程应注重学生职业的可持续发展性,因此,有必要在国家职业标准导向下基于区域经济对高职‘生物制药技术“课程的课程标准进行改革㊂一方面,随着经济转型和产业升级,企业㊁单位用人对从业人员的职业能力要求也在不断变化㊂职业标准体现的是社会对人才的需求,而课程标准则是规定高职院校的课程如何更有效地培养出社会所需要的人才㊂因此,职业标准与课程标准以社会需要的人才为平台,前者提供社会对人才的要求,后者将人才的要求反馈到高职课程中㊂另一方面,高职院校人才培养规格也影响经济发展及产业结构㊂高校需要供给能够适应高新技术产业迅速发展和产业结构转型升级所需要的高素质㊁高技能人才,才能为区域产业结构转型升级提供保障㊂因此,高职院校人才培养规格描述的课程标准,应该具备可持续发展性㊂2㊀高职‘生物制药技术“课程与国家标准对接的 三个结合2.1㊀教学模块与职业功能结合具体职业岗位(群)的职业能力需求有其内在的层次结构,课程教学目标和课程教学内容应该根据这种 层级层次 构建不同的教学模块㊂课程教学项目模块设定的思路为:首先,根据国家职业标准中的职业功能确定项目模块范围;再结合江苏省制药产业现状和发展趋势对项目模块进行调整;之后,对职业功能中的工作内容列表分析,并基于学生的认知规律,将不同职业功能中的共同工作任务部分整合,将不同工作任务根据职业功能划分成单独模块㊂根据国家职业标准,生物制药技术方向对应的职业工种包括五大类,具体为:生化药品制造工㊁发酵工程制药工㊁疫苗制品工㊁血液制品工和基因工程产品工[4]㊂在此基础上,我们对江苏省的制药产业进行了综合考察,通过资料查阅及调查研究发现,江苏省制药产业结构分布的实际情况是:在500多家正规制药企业中,只有不超过10家为疫苗专营或兼营企业㊂结合制药技术概念界定,将疫苗制品工对应的职业要求归并至 细胞工程 教学模块;此外,考虑到产业现状与升级需求,增加 酶工程 教学模块㊂综上所述,本着区域经济发展服务的需求,根据职业功能确定教学模块为血液制品与生化分离技术㊁天然生物材料与天然药物㊁发酵工程技术与发酵药物㊁细胞工程技术与免疫技术药物㊁基因工程技术与基因药物㊁酶工程技术与生化反应制药㊂具体见图1㊂图1㊀职业功能与教学模块对应表Fig.1㊀Correspondence of professional functions andteaching modules在每一教学模块中,基于各个岗位的工作内容描述,同时充分考虑与前导课程可能存在的重复性及与后续课程存在的衔接性,制定各个模块的教学目标,并确定教学内容㊂例如,发酵工程制药工的职业描述中主要包括10个方面[3],其中,诸如 使用消毒锅或消毒柜等,对培养基㊁压缩空气或其他材料㊁设备㊁器皿等进行消毒㊁灭菌 ㊁ 采用微生物方法培养㊁制备各级生产菌种,复壮㊁选育优质高产生产菌株 等方面主要属于前导课程‘微生物技术及应用“教学范畴㊂而诸如 使用固液分离设备进行发酵液或浸提液的固液分离 ㊁ 使用溶剂或交换树脂等进行有效药用成分的提取和纯化 等职业能力要求也是后续课程‘药物分离与纯化“的教学目标㊂因此,综合考虑后将发酵工程技术与发酵药物这一个模块的教学目标确定为:了解发酵工程技术的概念,掌握发酵工程制药的一般工艺流程及技术特点,熟悉主要的发酵技术药物及生产工艺㊂并以典型发酵工程产品,包括抗生素㊁维生素㊁氨基酸等为教学载体,通过这些药品生产工艺的讲解达到职业标准中 操作发酵设备和控制仪器㊁仪表,根据发酵代谢指标适当调节发酵工艺条件,完成发酵 加入工具酶和中间体,控制工艺条件,完成抗生素的酶解㊁转化工序 等职业能力要求㊂2.2㊀教学项目与岗位工作任务结合将真实工作任务融入教学体系已成为高职院校课程开发和改革的共识[5-6]㊂考虑到与地区医药产业发展相适应的问题,需要确定合适的工作任务以便实现情境与岗位对接㊂而实施基于正式工作工程的项目,其目标是引导学生处于一个自己想要去了解的境地,让学生能以相对积极的态度去做原本可能并不喜欢做的东西,在完成相关项目的过程中重新构建知识㊂通过一门课程所呈现的多个教学项目的实施和训练,实现学生从 学生 到 企业员工 身份的转变,发展职业能力㊂以 青霉素的发酵生产 这一教学项目为例㊂青霉素高效㊁低毒㊁临床应用广泛,是人类历史上发现的第一种抗生素,也是学生在生活中经常能接触到的一类药品㊂通过微生物发酵是生产青霉素的主要途径之一,其工艺控制难度较大,但对于其他抗生素类药品发酵工艺的学习具有示范性㊂通过与企第49卷第2期谢承佳,等:高职‘生物制药技术“课程与国家职业标准对接的 三个结合 125㊀业沟通确定职业能力需求,结合参考‘药品生产质量管理规范“及发酵制药工职业标准,确定教学重点及教学目标(见表1)㊂同时,结合企业生产示例设定教学情境:东方梦想科技园生物工程有限公司是完全按照GMP要求建造的现代化生物工程公司,拥有设施先进的研发中心,发酵车间,生物分离车间,三废处理车间,动力车间,大型仓库等,公司具有8个200顿的发酵罐,青霉素年产量为2100吨㊂学生是东方梦想科技园生物有限公司的一线生产操作人员,日常工作内容为青霉素的发酵生产㊂在教学实施环节,遵循正常工作流程,并考虑技能级次,确定教学顺序为 发酵环境要求 ㊁ 发酵流程认知 ㊁ 发酵参数控制 ㊁ 发酵生产对接 ㊂在实践环节,利用智慧教室及仿真软件打造虚实融合的教学环境,通过丰富的活动设计,并配合各种激励性措施和反思性活动,使真实工作任务支持学习的功能得以充分有效发挥㊂例如,在学生操作仿真软件的过程中,设定两人为一小组,模拟真实生产过程,一人扮演中控室人员,主要负责DCS操作,一人扮演工艺员,主要负责监督管理㊂通过这样的分工合作互助互提,激发团队潜能,培养学生的职业观㊂表1㊀青霉素的发酵生产教学要求Table1㊀Teaching requirements for fermentationproduction of penicillin青霉素的发酵生产教学情境学生是东方梦想科技园生物有限公司的一线操作人员,该公司青霉素年产量为2100吨教学目标知识目标:能理解青霉素发酵工艺流程,会分析各个参数之间的影响及联系;技能目标:能根据监测参数判断发酵趋势并进行正确的操作处理;素养目标:能按照岗位职责要求,遵守生产纪律,完成各项生产任务教学重点青霉素发酵的工艺操作教学难点青霉素发酵工艺参数的控制在教学项目实施过程中,结合工作情境设计教学方法㊂即:考虑在实际工作情境中知识和技能的传递情境,并充分考虑教学规律及其他客观因素,设计教学方法㊂例如,对于在实际工作岗位中以语言传递为主要方式的学习过程,可采用讲授法等;以实际感知为主的知识形成过程,在授课中可采用演示法㊁参观法㊁角色扮演法等;以实际训练为主的技能培养过程,可采用理实一体化教学㊁仿真教学等㊂此外,创新精神是企业的核心竞争力,而企业的创新来自员工的积极参与意识㊁勇气和能力㊂因此,在教学中,我们也鼓励教师多用㊁用好诸如探究法㊁讨论法等以引导探究为主的方法㊂2.3㊀教学考核与职业技能鉴定结合职业技能鉴定是国家职业资格证书制度的重要组成部分,是对劳动者从事某种职业所应掌握的技术理论知识和实际操作能力做出客观的测量和评价㊂将职业技能鉴定的相关内容融入教学考核,有助于强调学生将所学知识和技能在实践中加以应用,积极引导学生自主学习,强化学生动手能力㊁职业素养和工程意识[7]㊂首先,在理论教学方面,将教学内容的知识点与职业标准中的考点对接㊂例如,发酵制药工的职业描述之一是 采用微生物方法培养㊁制备各级生产菌种,复壮㊁选育优质高产生产菌株 ,其中涉及到的知识点包括:微生物的生长㊁接种技术㊁菌种的扩大培养㊁菌种保存㊁菌种的复壮㊁菌种的选育等,在平时的教学中,我们就将这些内容与教学案例相结合或作为单独知识点进行讲解,既避免了职业技能鉴定时再花费大量时间再进行系统培训,从而避免了教育资源的重复和浪费;又提高了学生通过职业鉴定的合格率㊂在实训基地建设方面,建设工学结合的实训基地,从而保障职业技能鉴定与高职教学活动的结合㊂我们采用 自主开发㊁校企共建 的建设模式,按照 生产型㊁职场化 的理念建成了一批集技能训练㊁项目化教学实施㊁技术开发与服务㊁社会培训与技能鉴定㊁技能竞赛㊁职业素质养成等功能于一体的完整的制药实训室体系,具体包括有机合成㊁生物发酵㊁化学制药㊁药物制剂㊁分析测试等涵盖药品生产技术的上㊁中㊁下游,其中包含两个江苏省的省级研发和测试中心㊂同时,在校外实训基地建设方面,我们也与包括江苏扬农集团有限公司㊁扬州联博药业有限公司㊁扬子江药业集团有限公司在内的紧密合作企业分地区㊁分层次建成了工学交替㊁顶岗实习㊁产学研结合的10余个校外实训实习基地㊂校内和校外实训基地的建成为有效保障了工学结合背景下职业技能鉴定与教学活动的结合㊂同时,通过良好的职业氛围,培养学生爱岗敬业㊁团结互助的职业素养㊂3㊀结㊀语综上所述,高等职业教育课程与国家职业标准的对接研究,需依据职业标准,以区域经济的实际需求为落脚点和出发点㊂在高职‘生物制药技术“课程的改革中,我们遵循此原则,做到了教学模块与职业功能结合㊁教学项目与岗位工作任务结合㊁教学考核与职业技能鉴定结合,切实提高药品生产技术专业学生的职业核心素养,为学生今后的职业发展奠定坚实的基础㊂参考文献[1]㊀国家中长期教育改革和发展规划纲要(2010-2020年)[OL]./publicfiles/business/htmlfiles/moe/info_list/ 201407/xxgk_171904.html?authkey=gwbux.[2]㊀国家药品监督管理局[OL]./datasearchcnda/face3/dir.html.[3]㊀国家职业技能标准编制技术规程(2018年版)[M].北京,2018.[4]㊀江苏省职业技能鉴定网[OL]./jdfww_bak/zcfg/zsglyjdsf/.[5]㊀关艳阁.现代国家职业标准导向下的高职课程改革研究[D].广州:广东技术师范学院,2015.[6]㊀戴有华,于泓,刘旭.高职机制专业课程教学内容与国家职业标准对接研究[J].职业教育研究,2013,(9):11-13.[7]㊀李慧丽.我国高职院校课程内容与职业标准对接的研究[D].上海:华东理工大学,2016.。
粒子群优化算法及其应用
华中科技大学 硕士学位论文 粒子群优化算法及其应用 姓名：王雁飞 申请学位级别：硕士 专业：软件工程 指导教师：陆永忠 20081024
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课题研究现状
粒子群优化研究现状 粒子群优化算法是 1995 年由 Kennedy 和 Eberhart 源于对鸟群和鱼群捕食行为的
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华 中 科 技 大 学 硕 士 学 位 论 文
简化社会模型的模拟而提出的一种基于群集智能的演化计算技术[1,2]。该算法具有并 行处理、鲁棒性好等特点，能以较大的概率找到问题的全局最优解，且计算效率比 传统随机方法高，其最大的优势在于实现容易、收敛速度快，而且有深刻的智能背 景，既适合科学研究，又适合工程应用。因此，PSO 一经提出立刻引起了演化计算 领域研究者的广泛关注，并在短短几年时间里涌现出大量的研究成果，在函数优化、 神经网络训练、模糊系统控制、分类、模式识别、信号处理、机器人技术等领域获 得了成功应用。 PSO 算法是基于群集智能理论的优化算法，通过群体中粒子间的合作与竞争产 生的群体智能指导优化搜索。与进化算法比较，粒子群优化算法不仅保留了基于种 群的全局搜索策略，而且又避免了复杂的遗传操作，它特有的记忆使其可以动态跟 踪当前的搜索情况调整其搜索策略。与进化算法比较，PSO 算法是一种更高效的并 行搜索算法，但其不足之处是在某些初始化条件下易陷入局部最优，且搜索精度比 遗传算法低[3]。 由于 PSO 算法概念简单，实现容易，短短几年时间，PSO 算法便获得了很大的 发展，但是，其数学基础不完善，实现技术不规范，在适应度函数选取、参数设置、 收敛理论等方面还存在许多需要深入研究的问题。文献[4-6]展开了一系列研究，取得 了一些建设性的成果，如关于算法收敛性的分析。围绕 PSO 的实现技术和数学理论 基础，以 Kennedy 和 Eberhart 为代表的许多专家学者一直在对 PSO 做深入的探索， 尤其在实现技术方面，提出了各种改进版本的 PSO。 对 PSO 参数的研究，研究最多的是关于惯性权重的取值问题。PSO 最初的算法 是没有惯性权重的， 自从 PSO 基本算法中对粒子的速度和位置更新引入惯性权重[7,8]， 包括 Eberhart、Shi 等在内的许多学者对其取值方法和取值范围作了大量的研究[9-11]。 目前大致可分为固定惯性权重取值法、线性自适应惯性权重取值法、非线性惯性权 重取值法[12-14]等。 PSO 是一种随机优化技术，其实现技术与遗传算法(GA)非常相似，受 GA 的启 发，人们提出多种改进的 PSO 算法，如带交叉算子的 PSO、带变异算子的 PSO、带 选择算子的 PSO 等等。 文献[15]在粒子群每次迭代后， 通过交叉来生成更优秀的粒子，
模拟ai英文面试题目及答案
模拟ai英文面试题目及答案模拟AI英文面试题目及答案1. 题目: What is the difference between a neural network anda deep learning model?答案: A neural network is a set of algorithms modeled loosely after the human brain that are designed to recognize patterns. A deep learning model is a neural network with multiple layers, allowing it to learn more complex patterns and features from data.2. 题目: Explain the concept of 'overfitting' in machine learning.答案: Overfitting occurs when a machine learning model learns the training data too well, including its noise and outliers, resulting in poor generalization to new, unseen data.3. 题目: What is the role of a 'bias' in an AI model?答案: Bias in an AI model refers to the systematic errors introduced by the model during the learning process. It can be due to the choice of model, the training data, or the algorithm's assumptions, and it can lead to unfair or inaccurate predictions.4. 题目: Describe the importance of data preprocessing in AI.答案: Data preprocessing is crucial in AI as it involves cleaning, transforming, and reducing the data to a suitableformat for the model to learn effectively. Proper preprocessing can significantly improve the performance of AI models by ensuring that the input data is relevant, accurate, and free from noise.5. 题目: How does reinforcement learning differ from supervised learning?答案: Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize a reward signal. It differs from supervised learning, where the model learns from labeled data to predict outcomes based on input features.6. 题目: What is the purpose of a 'convolutional neural network' (CNN)?答案: A convolutional neural network (CNN) is a type of deep learning model that is particularly effective for processing data with a grid-like topology, such as images. CNNs use convolutional layers to automatically and adaptively learn spatial hierarchies of features from input images.7. 题目: Explain the concept of 'feature extraction' in AI.答案: Feature extraction in AI is the process of identifying and extracting relevant pieces of information from the raw data. It is a crucial step in many machine learning algorithms, as it helps to reduce the dimensionality of the data and to focus on the most informative aspects that can be used to make predictions or classifications.8. 题目: What is the significance of 'gradient descent' in training AI models?答案: Gradient descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In the context of AI, it is used to minimize the loss function of a model, thus refining the model's parameters to improve its accuracy.9. 题目: How does 'transfer learning' work in AI?答案: Transfer learning is a technique where a pre-trained model is used as the starting point for learning a new task. It leverages the knowledge gained from one problem to improve performance on a different but related problem, reducing the need for large amounts of labeled data and computational resources.10. 题目: What is the role of 'regularization' in preventing overfitting?答案: Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function, which discourages overly complex models. It helps to control the model's capacity, forcing it to generalize better to new data by not fitting too closely to the training data.。
基于SPO语义三元组的自闭症谱系障碍药物知识发现
基于SPO语义三元组的自闭症谱系障碍药物知识发现吕艳华，赵宏霞，李琦，梁傲雪，于琦山西医科大学管理学院，山西 030001Drug knowledge discovery for autism spectrum disorders based on SPO predicationsLYU Yanhua, ZHAO Hongxia, LI Qi, LIANG Aoxue, YU QiSchool of Management, Shanxi Medical University, Shanxi 030001 ChinaCorresponding Author LYUYanhua,E⁃mail:******************Abstract Objective：To extract SPO（Subject⁃Predicate⁃Object，SPO） from literature related to Autism Spectrum Disorders（ASD） using semantic mining technology and construct a knowledge graph of ASD drug entities，to explore the potential drug for the treatment of ASD at a deeper level， and provide new ideas for discovering valuable potential drugs for other diseases（https：//）.Methods：Using the tools SemRep and Metamap based on the Unified Medical Language System （ULMS） to process ASD literature records and obtain SPO of ASD drug entities.The Neo4j database was used for knowledge storage to construct an ASD drug entities knowledge ing three semantic pathways to discovery ASD drug knowledge based on the knowledge graph.Then verified and analyzed the effectiveness of the results in the clinical trials databases.Results：The SPO obtained includes 1 262 head entities， 687 tail entities， and 18 entity relationships.A total of 32 drugs were discovered through three semantic pathways，27 potential drugs for ASD was screened out，and 19 drugs can be validated in the clinical trials databases.Conclusions：The knowledge discovery of ASD drugs based on knowledge graph which built by SPO can provide a certain theoretical and methodological basis for drug repositioning，provide new ideas for traditional drug discovery，and provide decision support for clinical experiments and scientific research.Keywords autism spectrum disorders； knowledge graph； semantic mining； drug repositioning摘要目的:运用语义挖掘技术抽取自闭症相关文献中的三元组并构建自闭症药物实体知识图谱，深层次开展自闭症治疗的潜力药物知识发现，同时也为其他疾病发现有价值的潜在治疗药物提供新思路。
人工智能英汉
人工智能英汉Aβα-Pruning, βα-剪枝, (2) Acceleration Coefficient, 加速系数, (8) Activation Function, 激活函数, (4) Adaptive Linear Neuron, 自适应线性神经元,(4)Adenine, 腺嘌呤, (11)Agent, 智能体, (6)Agent Communication Language, 智能体通信语言, (11)Agent-Oriented Programming, 面向智能体的程序设计, (6)Agglomerative Hierarchical Clustering, 凝聚层次聚类, (5)Analogism, 类比推理, (5)And/Or Graph, 与或图, (2)Ant Colony Optimization (ACO), 蚁群优化算法, (8)Ant Colony System (ACS), 蚁群系统, (8) Ant-Cycle Model, 蚁周模型, (8)Ant-Density Model, 蚁密模型, (8)Ant-Quantity Model, 蚁量模型, (8)Ant Systems, 蚂蚁系统, (8)Applied Artificial Intelligence, 应用人工智能, (1)Approximate Nondeterministic Tree Search (ANTS), 近似非确定树搜索, (8) Artificial Ant, 人工蚂蚁, (8)Artificial Intelligence (AI), 人工智能, (1) Artificial Neural Network (ANN), 人工神经网络, (1), (3)Artificial Neural System, 人工神经系统，(3) Artificial Neuron, 人工神经元, (3) Associative Memory, 联想记忆, (4) Asynchronous Mode, 异步模式, (4) Attractor, 吸引子, (4)Automatic Theorem Proving, 自动定理证明,(1)Automatic Programming, 自动程序设计, (1) Average Reward, 平均收益, (6) Axon, 轴突, (4)Axon Hillock, 轴突丘, (4)BBackward Chain Reasoning, 逆向推理, (3) Bayesian Belief Network, 贝叶斯信念网, (5) Bayesian Decision, 贝叶斯决策, (3) Bayesian Learning, 贝叶斯学习, (5) Bayesian Network贝叶斯网, (5)Bayesian Rule, 贝叶斯规则, (3)Bayesian Statistics, 贝叶斯统计学, (3) Biconditional, 双条件, (3)Bi-Directional Reasoning, 双向推理, (3) Biological Neuron, 生物神经元, (4) Biological Neural System, 生物神经系统, (4) Blackboard System, 黑板系统, (8)Blind Search, 盲目搜索, (2)Boltzmann Machine, 波尔兹曼机, (3) Boltzmann-Gibbs Distribution, 波尔兹曼-吉布斯分布, (3)Bottom-Up, 自下而上, (4)Building Block Hypotheses, 构造块假说, (7) CCell Body, 细胞体, (3)Cell Membrane, 细胞膜, (3)Cell Nucleus, 细胞核, (3)Certainty Factor, 可信度, (3)Child Machine, 婴儿机器, (1)Chinese Room, 中文屋, (1) Chromosome, 染色体, (6)Class-conditional Probability, 类条件概率,(3), (5)Classifier System, 分类系统, (6)Clause, 子句, (3)Cluster, 簇, (5)Clustering Analysis, 聚类分析, (5) Cognitive Science, 认知科学, (1) Combination Function, 整合函数, (4) Combinatorial Optimization, 组合优化, (2) Competitive Learning, 竞争学习, (4) Complementary Base, 互补碱基, (11) Computer Games, 计算机博弈, (1) Computer Vision, 计算机视觉, (1)Conflict Resolution, 冲突消解, (3) Conjunction, 合取, (3)Conjunctive Normal Form (CNF), 合取范式,(3)Collapse, 坍缩, (11)Connectionism, 连接主义, (3) Connective, 连接词, (3)Content Addressable Memory, 联想记忆, (4) Control Policy, 控制策略, (6)Crossover, 交叉, (7)Cytosine, 胞嘧啶, (11)DData Mining, 数据挖掘, (1)Decision Tree, 决策树, (5) Decoherence, 消相干, (11)Deduction, 演绎, (3)Default Reasoning, 默认推理（缺省推理）,(3)Defining Length, 定义长度, (7)Rule (Delta Rule), 德尔塔规则, 18(3) Deliberative Agent, 慎思型智能体, (6) Dempster-Shafer Theory, 证据理论, (3) Dendrites, 树突, (4)Deoxyribonucleic Acid (DNA), 脱氧核糖核酸, (6), (11)Disjunction, 析取, (3)Distributed Artificial Intelligence (DAI), 分布式人工智能, (1)Distributed Expert Systems, 分布式专家系统,(9)Divisive Hierarchical Clustering, 分裂层次聚类, (5)DNA Computer, DNA计算机, (11)DNA Computing, DNA计算, (11) Discounted Cumulative Reward, 累计折扣收益, (6)Domain Expert, 领域专家, (10) Dominance Operation, 显性操作, (7) Double Helix, 双螺旋结构, (11)Dynamical Network, 动态网络, (3)E8-Puzzle Problem, 八数码问题, (2) Eletro-Optical Hybrid Computer, 光电混合机, (11)Elitist strategy for ant systems (EAS), 精化蚂蚁系统, (8)Energy Function, 能量函数, (3) Entailment, 永真蕴含, (3) Entanglement, 纠缠, (11)Entropy, 熵, (5)Equivalence, 等价式, (3)Error Back-Propagation, 误差反向传播, (4) Evaluation Function, 评估函数, (6) Evidence Theory, 证据理论, (3) Evolution, 进化, (7)Evolution Strategies (ES), 进化策略, (7) Evolutionary Algorithms (EA), 进化算法, (7) Evolutionary Computation (EC), 进化计算,(7)Evolutionary Programming (EP), 进化规划,(7)Existential Quantification, 存在量词, (3) Expert System, 专家系统, (1)Expert System Shell, 专家系统外壳, (9) Explanation-Based Learning, 解释学习, (5) Explanation Facility, 解释机构, (9)FFactoring, 因子分解, (11)Feedback Network, 反馈型网络, (4) Feedforward Network, 前馈型网络, (1) Feasible Solution, 可行解, (2)Finite Horizon Reward, 横向有限收益, (6) First-order Logic, 一阶谓词逻辑, (3) Fitness, 适应度, (7)Forward Chain Reasoning, 正向推理, (3) Frame Problem, 框架问题, (1)Framework Theory, 框架理论, (3)Free-Space Optical Interconnect, 自由空间光互连, (11)Fuzziness, 模糊性, (3)Fuzzy Logic, 模糊逻辑, (3)Fuzzy Reasoning, 模糊推理, (3)Fuzzy Relation, 模糊关系, (3)Fuzzy Set, 模糊集, (3)GGame Theory, 博弈论, (8)Gene, 基因, (7)Generation, 代, (6)Genetic Algorithms, 遗传算法, (7)Genetic Programming, 遗传规划（遗传编程）,(7)Global Search, 全局搜索, (2)Gradient Descent, 梯度下降, (4)Graph Search, 图搜索, (2)Group Rationality, 群体理性, (8) Guanine, 鸟嘌呤, (11)HHanoi Problem, 梵塔问题, (2)Hebbrian Learning, 赫伯学习, (4)Heuristic Information, 启发式信息, (2) Heuristic Search, 启发式搜索, (2)Hidden Layer, 隐含层, (4)Hierarchical Clustering, 层次聚类, (5) Holographic Memory, 全息存储, (11) Hopfield Network, 霍普菲尔德网络, (4) Hybrid Agent, 混合型智能体, (6)Hype-Cube Framework, 超立方体框架, (8)IImplication, 蕴含, (3)Implicit Parallelism, 隐并行性, (7) Individual, 个体, (6)Individual Rationality, 个体理性, (8) Induction, 归纳, (3)Inductive Learning, 归纳学习, (5) Inference Engine, 推理机, (9)Information Gain, 信息增益, (3)Input Layer, 输入层, (4)Interpolation, 插值, (4)Intelligence, 智能, (1)Intelligent Control, 智能控制, (1) Intelligent Decision Supporting System (IDSS), 智能决策支持系统，(1) Inversion Operation, 倒位操作, (7)JJoint Probability Distribution, 联合概率分布,(5) KK-means, K-均值, (5)K-medoids, K-中心点, (3)Knowledge, 知识, (3)Knowledge Acquisition, 知识获取, (9) Knowledge Base, 知识库, (9)Knowledge Discovery, 知识发现, (1) Knowledge Engineering, 知识工程, (1) Knowledge Engineer, 知识工程师, (9) Knowledge Engineering Language, 知识工程语言, (9)Knowledge Interchange Format (KIF), 知识交换格式, (8)Knowledge Query and ManipulationLanguage (KQML), 知识查询与操纵语言，(8)Knowledge Representation, 知识表示, (3)LLearning, 学习, (3)Learning by Analog, 类比学习, (5) Learning Factor, 学习因子, (8)Learning from Instruction, 指导式学习, (5) Learning Rate, 学习率, (6)Least Mean Squared (LSM), 最小均方误差,(4)Linear Function, 线性函数, (3)List Processing Language (LISP), 表处理语言, (10)Literal, 文字, (3)Local Search, 局部搜索, (2)Logic, 逻辑, (3)Lyapunov Theorem, 李亚普罗夫定理, (4) Lyapunov Function, 李亚普罗夫函数, (4)MMachine Learning, 机器学习, (1), (5) Markov Decision Process (MDP), 马尔科夫决策过程, (6)Markov Chain Model, 马尔科夫链模型, (7) Maximum A Posteriori (MAP), 极大后验概率估计, (5)Maxmin Search, 极大极小搜索, (2)MAX-MIN Ant Systems (MMAS), 最大最小蚂蚁系统, (8)Membership, 隶属度, (3)Membership Function, 隶属函数, (3) Metaheuristic Search, 元启发式搜索, (2) Metagame Theory, 元博弈理论, (8) Mexican Hat Function, 墨西哥草帽函数, (4) Migration Operation, 迁移操作, (7) Minimum Description Length (MDL), 最小描述长度, (5)Minimum Squared Error (MSE), 最小二乘法,(4)Mobile Agent, 移动智能体, (6)Model-based Methods, 基于模型的方法, (6) Model-free Methods, 模型无关方法, (6) Modern Heuristic Search, 现代启发式搜索,(2)Monotonic Reasoning, 单调推理, (3)Most General Unification (MGU), 最一般合一, (3)Multi-Agent Systems, 多智能体系统, (8) Multi-Layer Perceptron, 多层感知器, (4) Mutation, 突变, (6)Myelin Sheath, 髓鞘, (4)(μ+1)-ES, (μ+1) -进化规划, (7)(μ+λ)-ES, (μ+λ) -进化规划, (7) (μ,λ)-ES, (μ,λ) -进化规划, (7)NNaïve Bayesian Classifiers, 朴素贝叶斯分类器, (5)Natural Deduction, 自然演绎推理, (3) Natural Language Processing, 自然语言处理,(1)Negation, 否定, (3)Network Architecture, 网络结构, (6)Neural Cell, 神经细胞, (4)Neural Optimization, 神经优化, (4) Neuron, 神经元, (4)Neuron Computing, 神经计算, (4)Neuron Computation, 神经计算, (4)Neuron Computer, 神经计算机, (4) Niche Operation, 生态操作, (7) Nitrogenous base, 碱基, (11)Non-Linear Dynamical System, 非线性动力系统, (4)Non-Monotonic Reasoning, 非单调推理, (3) Nouvelle Artificial Intelligence, 行为智能,(6)OOccam’s Razor, 奥坎姆剃刀, (5)(1+1)-ES, (1+1) -进化规划, (7)Optical Computation, 光计算, (11)Optical Computing, 光计算, (11)Optical Computer, 光计算机, (11)Optical Fiber, 光纤, (11)Optical Waveguide, 光波导, (11)Optical Interconnect, 光互连, (11) Optimization, 优化, (2)Optimal Solution, 最优解, (2)Orthogonal Sum, 正交和, (3)Output Layer, 输出层, (4)Outer Product, 外积法, 23(4)PPanmictic Recombination, 混杂重组, (7) Particle, 粒子, (8)Particle Swarm, 粒子群, (8)Particle Swarm Optimization (PSO), 粒子群优化算法, (8)Partition Clustering, 划分聚类, (5) Partitioning Around Medoids, K-中心点, (3) Pattern Recognition, 模式识别, (1) Perceptron, 感知器, (4)Pheromone, 信息素, (8)Physical Symbol System Hypothesis, 物理符号系统假设, (1)Plausibility Function, 不可驳斥函数（似然函数）, (3)Population, 物种群体, (6)Posterior Probability, 后验概率, (3)Priori Probability, 先验概率, (3), (5) Probability, 随机性, (3)Probabilistic Reasoning, 概率推理, (3) Probability Assignment Function, 概率分配函数, (3)Problem Solving, 问题求解, (2)Problem Reduction, 问题归约, (2)Problem Decomposition, 问题分解, (2) Problem Transformation, 问题变换, (2) Product Rule, 产生式规则, (3)Product System, 产生式系统, (3) Programming in Logic (PROLOG), 逻辑编程, (10)Proposition, 命题, (3)Propositional Logic, 命题逻辑, (3)Pure Optical Computer, 全光计算机, (11)QQ-Function, Q-函数, (6)Q-learning, Q-学习, (6)Quantifier, 量词, (3)Quantum Circuit, 量子电路, (11)Quantum Fourier Transform, 量子傅立叶变换, (11)Quantum Gate, 量子门, (11)Quantum Mechanics, 量子力学, (11) Quantum Parallelism, 量子并行性, (11) Qubit, 量子比特, (11)RRadial Basis Function (RBF), 径向基函数,(4)Rank based ant systems (ASrank), 基于排列的蚂蚁系统, (8)Reactive Agent, 反应型智能体, (6) Recombination, 重组, (6)Recurrent Network, 循环网络, (3) Reinforcement Learning, 强化学习, (3) Resolution, 归结, (3)Resolution Proof, 归结反演, (3) Resolution Strategy, 归结策略, (3) Reasoning, 推理, (3)Reward Function, 奖励函数, (6) Robotics, 机器人学, (1)Rote Learning, 机械式学习, (5)SSchema Theorem, 模板定理, (6) Search, 搜索, (2)Selection, 选择, (7)Self-organizing Maps, 自组织特征映射, (4) Semantic Network, 语义网络, (3)Sexual Differentiation, 性别区分, (7) Shor’s algorithm, 绍尔算法, (11)Sigmoid Function, Sigmoid 函数（S型函数）,(4)Signal Function, 信号函数, (3)Situated Artificial Intelligence, 现场式人工智能, (1)Spatial Light Modulator (SLM), 空间光调制器, (11)Speech Act Theory, 言语行为理论, (8) Stable State, 稳定状态, (4)Stability Analysis, 稳定性分析, (4)State Space, 状态空间, (2)State Transfer Function, 状态转移函数,(6)Substitution, 置换, (3)Stochastic Learning, 随机型学习, (4) Strong Artificial Intelligence (AI), 强人工智能, (1)Subsumption Architecture, 包容结构, (6) Superposition, 叠加, (11)Supervised Learning, 监督学习, (4), (5) Swarm Intelligence, 群智能, (8)Symbolic Artificial Intelligence (AI), 符号式人工智能（符号主义）, (3) Synapse, 突触, (4)Synaptic Terminals, 突触末梢, (4) Synchronous Mode, 同步模式, (4)TThreshold, 阈值, (4)Threshold Function, 阈值函数, (4) Thymine, 胸腺嘧啶, (11)Topological Structure, 拓扑结构, (4)Top-Down, 自上而下, (4)Transfer Function, 转移函数, (4)Travel Salesman Problem, 旅行商问题, (4) Turing Test, 图灵测试, (1)UUncertain Reasoning, 不确定性推理, (3)Uncertainty, 不确定性, (3)Unification, 合一, (3)Universal Quantification, 全称量词, (4) Unsupervised Learning, 非监督学习, (4), (5)WWeak Artificial Intelligence (Weak AI), 弱人工智能, (1)Weight, 权值, (4)Widrow-Hoff Rule, 维德诺-霍夫规则, (4)。
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Recently, Eberhart and Kennedy suggested a particle swarm optimization (PSO) based on the analogy of swarm of bird[4]. The algorithm, which is based on a metaphor of social interaction, searches a space by adjusting the trajectories of individual vectors, called ”particles ” as they are conceptualized as moving points in multidimensional space. The individual particles are drawn stochastically toward the position of their own previous best performance and the best previous performance of their neighbors. The main advantages of the PSO algorithm are summarized as: simple concept, easy implementation, robustness to control parameters, and computational efficiency when compared with mathematical algorithm and other heuristic optimization techniques. The original PSO has been applied to a learning problem of neural networks and function optimization problems, and efficiency of the method has been confirmed. In this paper, the objective is to investigate the capability of the PSO algorithm to discover classification rule with higher predictive accuracy and a much smaller rule list.
Abstract. Classification rule mining is one of the important problems in the emerging field of data mining which is aimed at finding a small set of rules from the training data set with predetermined targets. To efficiently mine the classification rule from databases, a novel classification rule mining algorithm based on particle swarm optimization (PSO) was proposed. The experimental results show that the proposed algorithm achieved higher predictive accuracy and much smaller rule list than other classification algorithm.
D.-S. Huang, L. Heutte, and M. Loog (Eds.): ICIC 2007, LNAI 4682, pp. 377–384, 2007. c Springer-Verlag Berlin Heidelberg 2007
378 Z. Wang, X. Sun, and D. Zhang
Classification rule mining is one of the important problems in the emerging field of data mining which is aimed at finding a small set of rules from the training data set with predetermined targets[2]. There are different classification algorithms used to extract relevant relationship in the data as decision trees that operate a successive partitioning of cases until all subsets belong to a single class. However, this operating way is impracticable except for trivial data sets. There are many other approaches for data classification, such as statistical and roughest approaches and neural networks. These classification techniques require significant expertise to work effectively but do not provide intelligible rules though they are algorithmically strong.
1 Introducቤተ መጻሕፍቲ ባይዱion
The current information age is characterized by a great expansion in the volume of data that are being generated and stored. Intuitively, this large amount of stored data contains valuable hidden knowledge, which could be used to improve the decision-making process of an organization. With the rapid growth in the amount of information stored in databases, the development of efficient and effective tools for revealing valuable knowledge hidden in these databases becomes more critical for enterprise decision making. One of the possible approaches to this problem is by means of data mining or knowledge discovery from databases (KDD)[1]. Through data mining, interesting knowledge can be extracted and the discovered knowledge can be applied in the corresponding field to increase the working efficiency and to improve the quality of decision making.
The rest of the paper is organized as follows. In the next section, we give a brief problem description about mining classification rule. In section 3, we present the basic idea and key techniques of the PSO algorithm. In section 4,the PSO-based classification rule mining algorithm is proposed. Section 5 reports experimental results when comparing with Ant-Miner[5] and GA-based classification algorithm across six data sets. Finally, the paper ends with conclusions and future research directions.
A PSO-Based Classification Rule Mining Algorithm
Ziqiang Wang, Xia Sun, and Dexian Zhang
School of Information Science and Engineering, Henan University of Technology, Zheng Zhou 450052, China wzqagent@
searches of the parameter space become computationally infeasible rapidly. The self-adaptability of evolutionary algorithms based on population is extremely appealing when tackling the tasks of data mining. Thus it is natural to direct attention to heuristic approaches to find a ”good-enough” solution to combat the classification problem. In recent years, evolutionary algorithms(such as genetic algorithm,immune algorithm and ant colony algorithm) have emerged as promising techniques to discover useful and interesting knowledge from databases[3]. Especially, there are numerous attempts to apply genetic algorithms(GAs) in data mining to accomplish classification tasks. In addition, the particle swarm optimization (PSO) algorithm[4], which has emerged recently as a new metaheuristic derived from nature, has attracted many researchers’ interests. The algorithm has been successfully applied to several minimization optimization problems and neural network training. Nevertheless, the use of the algorithm for mining classification rule in the context of data mining is still a research area where few people have tried to explore.