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  • [外文期刊] Li Li Huiping Chen William Zhu Xiaozhong Zhu 《Journal of information and computational science》, 2014 2
    Research in machine learning, statistics and related fields has produced a wide variety of algorithms for cost-sensitive learning. Some of these algorithms are devoted to minimal test cost reduction fo...
    Cost-sensitive LearningAttribute ReductionTesting Time CostWaiting Cost
  • [外文期刊] Zhongyi Sun Haijun Ding William Zhu Xiaozhong Zhu 《Journal of information and computational science》, 2014 1
    Feature selection is an important task in machine learning and data mining. In real world applications, time and money are required to obtain features of objects. Many existing works have been develope...
    Feature SelectionTesting Time CostWaiting CostConstraintConditional Entropy
  • [外文会议] Yanqing Zhu William Zhu 2014
    As one of three basic theories of granular computing, rough set theory provides a useful tool for dealing with the granularity in information systems. Covering-based rough set theory is a generalizatio...
    CoveringRough setGranular computingApproximation operatorSet-valued information system
  • [外文会议] Bin Yang William Zhu 2014
    As a technique for granular computing, rough sets deal with the vagueness and granularity in information systems. Covering-based rough sets are natural extensions of the classical rough sets by relaxin...
    Granular computingCoveringRough setNeighborhoodInclusion degreeNetwork security
  • [外文会议] Junxia Niu Hong Zhao William Zhu 2014
    Minimal test cost attribute reduction is an important problem in cost-sensitive learning since it reduces the dimensionality of the attributes space. To address this issue, many heuristic algorithms ha...
    Cost-sensitive learningGranular computingAttribute reductionTest costLogarithmic weighted algorithm
  • [外文会议] Anjing Fan Hong Zhao William Zhu 2014
    The minimal test cost attribute reduction is an important component in data mining applications, and plays a key role in cost-sensitive learning. Recently, several algorithms are proposed to address th...
    Cost-sensitive learningMinimal test costAttribute reductionGranular computingBiologically-inspired algorithm
  • [外文会议] Xu He Fan Min William Zhu 2014
    Recommender systems are popular in e-commerce as they provide users with items of interest. Existing top-K approaches mine the K strongest granular association rules for each user, and then recommend r...
    Granular computingRecommender systemGranule association rulesConfidenceSignificance
  • [外文期刊] Lirun Su William Zhu 《International journal of granular computing, rough sets and intelligent systems》, 2013 2
    Covering-based rough set theory is a useful tool to deal with inexact, uncertain or vague knowledge in information systems. In order to broaden the application and theoretical areas of rough sets and m...
    rough setcoveringupper approximation operatormatroidclosure operatorunary covering
  • [外文期刊] Jingqian Wang William Zhu 《Journal of applied mathematics》, 2013 Pt.7
    Rough sets provide an efficient tool for dealing with the vagueness and granularity in information systems. They are widely used in attribute reduction in data mining. There are many optimization issue...
    MatricesStructureSets
  • [外文期刊] Hong Zhao Fan Min William Zhu 《Journal of information and computational science》, 2013 13
    In data mining applications, one of the most fundamental problems is feature selection, which can improve the generalization capability of patterns. Recent developments in this field have led to a rene...
    Feature SelectionNormal Distribution Measurement ErrorsTest CostsMisclassification Costs
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