Induction of Selective Bayesian Classifiers.

Abstract

In this paper, we examine previous work on the naive Bayesian classifier and review its limitations, which include a sensitivity to correlated features. We respond to this problem by embedding the naive Bayesian induction scheme within an algorithm that carries out a greedy search through the space of features. We hypothesize that this approach will improve asymptotic accuracy in domains that involve correlated features without reducing the rate of learning in ones that do not. We report experimental results on six natural domains, including comparisons with decision-tree induction, that support these hypotheses. In closing, we discuss other approaches to extending naive Bayesian classifiers and outline some directions for future research. (AN)

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Document Details

Document Type
Technical Report
Publication Date
Aug 15, 1994
Accession Number
ADA292690

Entities

People

  • Pat Langley
  • Stephanie Sage

Tags

Communities of Interest

  • Autonomy
  • Engineered Resilient Systems
  • Human Systems

DTIC Thesaurus Topics

  • Accuracy
  • Acquisition
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Bayesian Networks
  • Breast Cancer
  • Computer Science
  • Data Sets
  • Learning
  • Machine Learning
  • Neural Networks
  • Pattern Recognition
  • Probability
  • Probability Distributions
  • Reasoning
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Space