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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52588

Title: Incremental rough set approach for hierarchical multicriteria classification
Authors: Chuan Luo
Tianrui Li
Hongmei Chen
Hamido Fujita
Zhang Yi
Contributors: 國立臺灣海洋大學:資訊工程學系
Keywords: Dominance-based rough sets
Incremental learning
Attribute value taxonomies
Rough approximation
Multicriteria classification
Date: 2018
Issue Date: 2019-11-22T01:41:39Z
Publisher: Information Sciences
Abstract: Abstract: Multicriteria classification refers to classify objects evaluated by a set of criteria to preference-ordered decision classes. Dominance-based rough set approach has been successfully introduced to express and reason inconsistencies with a dominance principle in multicriteria classification problems. Hierarchical attribute values exist extensively within many real-world applications, which provide a hierarchical form to organize, view and analyze data from different perspectives for accommodating the preference variability. In this study, we consider an extension of dominance-based rough set approach by applying an incremental learning technique for hierarchical multicriteria classification while attribute values dynamically vary across different levels of granulations. We formalize the dynamic characteristics of knowledge granules with the cut refinement and coarsening through attribute value taxonomies in the hierarchical multicriteria decision systems. In consequence, incremental algorithms for computing dominance-based rough approximations of preference-ordered decision classes are developed by applying the resulted prior-knowledge as the input, and only recomputing those outputs which depend on the changed attribute values. This paper presents the theoretical foundation of the proposed approach. Example analysis and experimental evaluation are also provided for illustration of the feasibility and efficiency.
Relation: 429 pp.72-87
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52588
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