Constructive meta-learning with machine learning method repositories

Hidenao Abe, Takahira Yamaguchi

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

17 Citations (Scopus)

Abstract

Here is discussed what is constructive meta-learning and how it goes well compared with selective meta-learning that already becomes popular. Selective meta-learning takes multiple learning schemes with the following different ways: bagging, boosting, cascading and stacking methods. On the other hand, constructive meta-learning constructs the learning scheme proper to a given data set. We have implemented constructive meta-learning by recomposing methods into learning schemes with mining (inductive learning) method repositories that come from decomposition of popular mining algorithms. To evaluate our constructive meta-learning, we have done the comparison of the performances of our constructive meta-learning and those of two stacking methods, using UCI/ML common data sets. It has shown us that our constructive meta-learning goes better than the two stacking methods. Furthermore, it turns out to be promising that we apply constructive meta-learning to meta-learner in selective meta-learning.

Original languageEnglish
Title of host publicationLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
EditorsB. Orchard, C. Yang, M. Ali
Pages502-511
Number of pages10
Volume3029
Publication statusPublished - 2004
Externally publishedYes
Event17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004 - Ottowa, Ont., Canada
Duration: 2004 May 172004 May 20

Other

Other17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004
CountryCanada
CityOttowa, Ont.
Period04/5/1704/5/20

Fingerprint

Learning systems
Decomposition

ASJC Scopus subject areas

  • Hardware and Architecture

Cite this

Abe, H., & Yamaguchi, T. (2004). Constructive meta-learning with machine learning method repositories. In B. Orchard, C. Yang, & M. Ali (Eds.), Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3029, pp. 502-511)

Constructive meta-learning with machine learning method repositories. / Abe, Hidenao; Yamaguchi, Takahira.

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). ed. / B. Orchard; C. Yang; M. Ali. Vol. 3029 2004. p. 502-511.

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

Abe, H & Yamaguchi, T 2004, Constructive meta-learning with machine learning method repositories. in B Orchard, C Yang & M Ali (eds), Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). vol. 3029, pp. 502-511, 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004, Ottowa, Ont., Canada, 04/5/17.
Abe H, Yamaguchi T. Constructive meta-learning with machine learning method repositories. In Orchard B, Yang C, Ali M, editors, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). Vol. 3029. 2004. p. 502-511
Abe, Hidenao ; Yamaguchi, Takahira. / Constructive meta-learning with machine learning method repositories. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). editor / B. Orchard ; C. Yang ; M. Ali. Vol. 3029 2004. pp. 502-511
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