2015年9月10日
Evolutionary multi-objective distance metric learning for multi-label clustering
2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
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- 開始ページ
- 2945
- 終了ページ
- 2952
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1109/CEC.2015.7257255
- 出版者・発行元
- Institute of Electrical and Electronics Engineers Inc.
In data mining and machine learning, the definition of the distance between two data points substantially affects clustering and classification tasks. We propose a distance metric learning (DML) method for multi-label clustering, that uses evolutionary multi-objective optimization and a cluster validity measure with a neighbor relation that simultaneously evaluates inter-and intra-clusters. The proposed method produces clustering results considering multiple class labels and allows the induction of knowledge regarding relations between class labels in multi-label clustering or between objective functions and elements in transform matrix. Experimental results have shown that the proposed DML method produces better transform matrices than single-objective optimization and is helpful in finding the attributes that affect the trade-off relationship among objective functions.
- ID情報
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- DOI : 10.1109/CEC.2015.7257255
- SCOPUS ID : 84963579856