Constructive meta-level feature selection method based on method repositories

Hidenao Abe, Takahira Yamaguchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings
PublisherSpringer Verlag
Number of pages11
ISBN (Print)3540332065, 9783540332060
Publication statusPublished - 2006
Externally publishedYes
Event10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006 - Singapore, Singapore
Duration: 2006 Apr 92006 Apr 12

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3918 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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