Oblivious Decision Trees and Abstract Cases,

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.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1994
Accession Number
ADA292577

Entities

People

  • Pat Langley
  • Stephanie Sage

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Algorithms
  • Artificial Intelligence
  • Breast Cancer
  • Classification
  • Contrast
  • Data Sets
  • Feature Selection
  • Information Operations
  • Intelligent Systems
  • Language
  • Learning
  • Machine Learning
  • Natural Languages
  • Test Sets
  • Training

Fields of Study

  • Computer science

Readers

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