Constructing inductive applications by meta-learning with method repositories

Hidenao Abe, Takahira Yamaguchi

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages576-585
Number of pages10
Volume2281
Publication statusPublished - 2002
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2281
ISSN (Print)03029743
ISSN (Electronic)16113349

Fingerprint

Meta-learning
Repository
Inductive Learning
Specification
Specifications
Learning algorithms
Learning Algorithm
UNIX
Chemical analysis
Design

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Abe, H., & Yamaguchi, T. (2002). Constructing inductive applications by meta-learning with method repositories. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2281, pp. 576-585). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2281).

Constructing inductive applications by meta-learning with method repositories. / Abe, Hidenao; Yamaguchi, Takahira.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2281 2002. p. 576-585 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2281).

Research output: Chapter in Book/Report/Conference proceedingChapter

Abe, H & Yamaguchi, T 2002, Constructing inductive applications by meta-learning with method repositories. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 2281, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2281, pp. 576-585.
Abe H, Yamaguchi T. Constructing inductive applications by meta-learning with method repositories. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2281. 2002. p. 576-585. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Abe, Hidenao ; Yamaguchi, Takahira. / Constructing inductive applications by meta-learning with method repositories. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2281 2002. pp. 576-585 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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