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International Conference on machine learning and Cybernetics (ICMLC)

作者： Chun-Ru Dong Daniel S. Yeung Xi-Zhao Wang Key Lab. of Machine Learning and Computational Intelligence Hebei University Baoding China Machine Learning and Cybernetics Research Center South China University of Technology Guangzhou China

ISBN: (纸本)9781479902590

Feature weighting, which is considered as an extension of feature selection techniques, has been successfully applied to improve the performance of clustering. Focusing on the clustering based on a similarity matrix, we design an optimization model to minimize the fuzziness of similarity matrix by learning feature weights. The objective of this model is to get a more reasonable result of clustering through minimizing the uncertainty (fuzziness and non-specificity) of similarity matrix. To solving this optimization model effectively, we propose a new searching approach which integrates together multiple evolution strategies of both differential evolution and dynamic differential evolution. The experimental results on several benchmark datasets show that the performance of the proposed method is significantly improved compared to that of gradient-descent-based approach in terms of five selected clustering evaluation indices, i.e., fuzziness of similarity matrix, intra-class similarity, inter-class similarity, ratio of intra-class similarity to inter-class similarity.

关键词： Abstracts Noise measurement

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Fast processing of foreign fiber images by image blocking

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Information Processing in Agriculture 2014年第1期1卷 2-13页

作者： Yutao Wu Daoliang Li Zhenbo Li Wenzhu Yang Beijing Engineering&Technology Research Center for Internet of Things in Agriculture China-EU Center for Information and Communication Technologies in Agriculture of China Agriculture UniversityBeijing100083PR China Key Lab of Machine Learning and Computational Intelligence College of Mathematics and Computer ScienceHebei UniversityBaoding071002PR China

In the textile industry,it is always the case that cotton products are constitutive of many types of foreign fibers which affect the overall quality of cotton *** the foundation of the foreign fiber automated inspection,image process exerts a critical impact on the process of foreign fiber *** paper presents a new approach for the fast processing of foreign fiber *** approach includes five main steps,image block,image predecision,image background extraction,image enhancement and segmentation,and image *** first,the captured color images were transformed into gray-scale images;followed by the inversion of gray-scale of the transformed images;then the whole image was divided into several ***,the subsequent step is to judge which image block contains the target foreign fiber image through image *** we segment the image block via OSTU which possibly contains target images after background eradication and image ***,we connect those relevant segmented image blocks to get an intact and clear foreign fiber target *** experimental result shows that this method of segmentation has the advantage of accuracy and speed over the other segmentation *** the other hand,this method also connects the target image that produce fractures therefore getting an intact and clear foreign fiber target image.

关键词： Cotton Foreign fibers Fast image processing Image block Image pre-decision Image connection

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ON THE APPLICATION OF ROUGH SETS TO DATA MINING IN ECONOMIC PRACTICE

ON THE APPLICATION OF ROUGH SETS TO DATA MINING IN ECONOMIC ...

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2009 International Conference on machine learning and Cybernetics(2009机器学习与控制论国际会议)

作者： QUN-FENG ZHANG SU-YUN ZHAO YUN-CHAO BAI Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Scie Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Scie College of Economics Hebei University Baoding 071002 China

Mathematical models play an important role in the studies of modern economics. But in many fields of economics, it is difficult to build mathematical models for complex phenomena. So data mining is getting more and more popular in discovering the potential pattern of economic knowledge from databases. As a powerful tool for data mining, rough set theory has been widely used. In this research, we draw guidelines from several cases of rough set application in economic practice. Furthermore, to avoid the drawbacks of the existing methods, we develop a methodology for rough analysis in economic sector by combining the advantages of the fuzzy variable precision rough set model.

关键词： Data mining Rough sets Reducts Fuzzy variable precision rough sets

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A text classification method based on the improved CR-tree and the weighted association rule

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Journal of computational Information Systems 2009年第6期5卷 1577-1584页

作者： Wang, Yu Zhao, Yao Yuan, Fang College of Mathematics and Computer Science Hebei University Baoding 071002 China Key Lab. in Machine Learning and Computational Intelligence of Hebei Province Baoding 071002 China

Classification based on association rules is a common and easily understand algorithm for text classification. To improve its classification accuracy, the key is to generate more effective rules. Sometimes, it will overdraw the role of some training texts. To avoiding this and generate more effective rules, this paper defines the Classification Path and proposes a new text classifier (CP-tree) based on CR-tree. When a new text is coming, to avoid overdrawing, association rules are generated through scanning Classification Path;in addition, to make the role of association rules in class prediction more reasonable, the association rules and weight for the new text are obtained not only according to the training texts, but also the new texts. Experimental results show that the accuracy of text classification is improved with the algorithm based on CP-tree. 1553-9105/ Copyright © 2009 Binary Information Press.

关键词： Classification (of information)

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Parallel implementation of apriori algorithm based on MapReduce

Parallel implementation of apriori algorithm based on MapRed...

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13th ACIS International Conference on Software Engineering, Artificial intelligence, Networking, and Parallel/Distributed Computing, SNPD 2012

作者： Li, Ning Zeng, Li He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100139 China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9780769547619

Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorithms that can handle large volumes of data becomes a challenging task due to the large databases. In this paper, we implement a parallel Apriori algorithm based on MapReduce, which is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes). The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware. © 2012 IEEE.

关键词： MapReduce

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Support vector machine for suppressing error method

Support vector machine for suppressing error method

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1st International Conference on Information Science and Engineering, ICISE2009

作者： Lu, Shuxia Shi, Pu Chen, Ming Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China Baoding Pascali Cable TV Integrater Information Network Co. Ltd. Baoding China College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (纸本)9780769538877

Support Vector machine (SVM) is sensitive to noises and outliers. For reducing the effect of noises and outliers, we propose a novel SVM for suppressing error function. The error function is limited to the interval of [0, 1]. The separation hypersurface is simplified and the margin of hypersurface is widened. Experimental results show that our proposed method is able to simultaneously increase the classification efficiency and the generalization ability of the SVM.

关键词： Support vector machines

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Markov property of Markov chains and its test

Markov property of Markov chains and its test

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Yu-Fen Zhang Qun-Feng Zhang Rui-Hua Yu Key Lab. for Machine Learning and Computational Intelligence Hebei University Baoding China Department of Information Technology Baoding University Baoding China

Markov chains, with Markov property as its essence, are widely used in the fields such as information theory, automatic control, communication techniques, genetics, computer sciences, economic administration, education administration, and market forecasts. While using Markov chains to predict the future events, we must test the Markov property of random variable sequences of the past statistic data. Only when the random variable sequences satisfy the Markov property, can the prediction could be precise. This paper discusses the concept of Markov property and its features, studies its test method, and by example demonstrates the effectiveness of this prediction method.

关键词： Markov processes Random variables machine learning Biological system modeling Prediction methods Water resources

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Updating Approximations of VPRS Model based on Dominance Relations

Updating Approximations of VPRS Model based on Dominance Rel...

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作者： Jiajia HOU Yan LI Yan ZHAN Liang WANG Key Lab.In Machine Learning and Computational Intelligence College of Mathematics and Computer ScienceHebei University

In real world problems, the collected data vary from time to time, and therefore, the approximations of a concept by a variable precision rough set model(VPRS) should be correspondingly updated. This paper focuses on developing incremental method to update set approximations of VPRS based on dominance relations. Under dynamic environments where an object is inserted or deleted, we present the updating principles and then develop the incremental method for updating approximation sets. The related theoretical results are presented with proofs, and illustrative examples are also given to support the effectiveness of the proposed method.

关键词： Variable Precision Rough Set Dominance Relation Set Approximation Dynamic Information Systems Incremental Update

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Incrementally Updating Method in Dominance- Based Rough Set Approach

Incrementally Updating Method in Dominance- Based Rough Set ...

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作者： Yan LI Xiaoqing LIU Jiajia HOU Key Lab.In Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University

Dominance-based rough set approach(DRSA) can handle the attributes with preference orders, and therefore it has been widely applied in multi-criteria decision making problems. In real applications, the collected information is updated from time to time which results in dynamic information systems, especially when the attributes or objects are inserted or deleted. The traditional DRSA needs to update the set approximations whenever the information systems change, which decreases the method efficiency greatly. For classification problems with multiple criteria, this paper presents incremental algorithms to update set approximations when an object is inserted or deleted, which is expected to be more efficient than computing the approximations from the scratch. The related theoretical results are presented with proofs, and illustrative examples are also given to support the effectiveness of the proposed incremental method.

关键词： Rough Set Dominance Relation Set Approximation Dynamic Information Systems Incremental Update

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Variable-precision Rough Set Approach based on Extended Dominance Relations

Variable-precision Rough Set Approach based on Extended Domi...

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作者： Yan LI Yongfei JIN Key Lab.Of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University

According to the definition of dominance relation, an object x is said to dominate another object y only when x dominates y on all attributes, which is too strict especially when the number of attributes is large. To cope with this problem, the extended dominance-based rough set model has been developed by introducing a parameter to the concept of traditional dominance relationship in the reported literature. However, in this extended model, the definitions of lower and upper approximations are the same to the traditional model, which may affect the decision making process. In this paper, we introduce the idea of variable precision to the extended dominance rough set model for better fault tolerance ability. The impact of the parameter on decision results with respect to testing accuracy is studied. Finally, an example is given and the experimental results on UCI data are also shown to support the effectiveness of the proposed method.

关键词： Extended dominance relation Variable precision rough set Dominant sets Approximation sets

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