Constructing inductive applications by meta-learning with method repositories

Hidenao Abe, Takahira Yamaguchi

研究成果: Chapter

2 被引用数 (Scopus)

抄録

Here is presented CAMLET that is a platform for automatic composition of inductive applications with method repositories that organize many inductive learning methods. CAMLET starts with constructing a basic design specification for inductive applications with method repositories and data type hierarchy that are specific to inductive learning algorithms. After instantiating the basic design with a given data set into a detailed design specification and then compiling it into codes, CAMLET executes them on computers. CAMLET changes the constructed specification until it goes beyond the goal accuracy given from a user. After having implemented CAMLET on UNIX platforms with Perl and C languages, we have done the case studies of constructing inductive applications for eight different data sets from the StatLog project and have compared the accuracies of the inductive applications composed by CAMLET with all the accuracies from popular inductive learning algorithms. The results have shown us that the inductive applications composed by CAMLET take the first accuracy on the average.

本文言語English
ホスト出版物のタイトルProgress in Discovery Science
編集者Setsuo Arikawa, Ayumi Shinohara
ページ576-585
ページ数10
出版ステータスPublished - 2002 12 1
外部発表はい

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)
2281
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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