Accession Number : ADA292577

Title :   Oblivious Decision Trees and Abstract Cases,

Descriptive Note : Interim rept. 1 Mar-31 Jul 94,

Corporate Author : INSTITUTE FOR THE STUDY OF LEARNING AND EXPERTISE PALO ALTO CA

Personal Author(s) : Langley, Pat ; Sage, Stephanie

PDF Url : ADA292577

Report Date : 01 AUG 1994

Pagination or Media Count : 7

Abstract : In this paper, we address the problem of case-based learning in the presence of irrelevant features. We review previous work on attribute selection and present a new algorithm, OBLIVION, that carries out greedy pruning of oblivious decision trees, which effectively store a set of abstract cases in memory. We hypothesize that this approach will efficiently identify relevant features even when they interact, as in parity concepts. We report experimental results on artificial domains that support this hypothesis, and experiments with natural domains that show improvement in some cases but not others. In closing, we discuss the implications of our experiments, consider additional work on irrelevant features, and outline some directions for future research.

Descriptors :   *TREES, ALGORITHMS, EXPERIMENTAL DATA, REPORTS, ABSTRACTS, ARTIFICIAL INTELLIGENCE, HYPOTHESES, PARITY.

Subject Categories : Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE