Generation of attributes for learning algorithms

Yuh-Jyh Hu*, Dennis Kibler

*Corresponding author for this work

Research output: Contribution to conferencePaper

27 Scopus citations

Abstract

Inductive algorithms rely strongly on their representational biases. Constructive induction can mitigate representational inadequacies. This paper introduces the notion of a relative gain measure and describes a new constructive induction algorithm (GALA) which is independent of the learning algorithm. Unlike most previous research on constructive induction, our methods are designed as preprocessing step before standard machine learning algorithms are applied. We present the results which demonstrate the effectiveness of GALA on artificial and real domains for several learners: C4.5, CN2, perceptron and backpropagation.

Original languageEnglish
Pages806-811
Number of pages6
StatePublished - 1 Dec 1996
EventProceedings of the 1996 13th National Conference on Artificial Intelligence, AAAI 96. Part 1 (of 2) - Portland, OR, USA
Duration: 4 Aug 19968 Aug 1996

Conference

ConferenceProceedings of the 1996 13th National Conference on Artificial Intelligence, AAAI 96. Part 1 (of 2)
CityPortland, OR, USA
Period4/08/968/08/96

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    Hu, Y-J., & Kibler, D. (1996). Generation of attributes for learning algorithms. 806-811. Paper presented at Proceedings of the 1996 13th National Conference on Artificial Intelligence, AAAI 96. Part 1 (of 2), Portland, OR, USA, .