Constructive meta-level feature selection method based on method repositories

Hidenao Abe, Takahira Yamaguchi

研究成果: Conference contribution

抄録

Feature selection is one of key issues related with data preprocessing of classification task in a data mining process. Although many efforts have been done to improve typical feature selection algorithms (FSAs), such as filter methods and wrapper methods, it is hard for just one FSA to manage its performances to various datasets. To above problems, we propose another way to support feature selection procedure, constructing proper FSAs to each given dataset. Here is discussed constructive meta-level feature selection that re-constructs proper FSAs with a method repository every given datasets, de-composing representative FSAs into methods. After implementing the constructive meta-level feature selection system, we show how constructive meta-level feature selection goes well with 32 UCI common data sets, comparing with typical FSAs on their accuracies. As the result, our system shows the highest performance on accuracies and the availability to construct a proper FSA to each given data set automatically.

本文言語English
ホスト出版物のタイトルAdvances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings
出版社Springer Verlag
ページ70-80
ページ数11
ISBN(印刷版)3540332065, 9783540332060
DOI
出版ステータスPublished - 2006
イベント10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006 - Singapore, Singapore
継続期間: 2006 4 92006 4 12

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
3918 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006
CountrySingapore
CitySingapore
Period06/4/906/4/12

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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