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, ...
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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.
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 inspecti...
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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.
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 mo...
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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.
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 ov...
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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...
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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.
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 algorith...
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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, educatio...
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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.
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 ...
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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.
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 infor...
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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.
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 ...
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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.
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