Empirical Analysis and Refinement of Expert System Knowledge Bases

Abstract

Classification methods from statistical pattern recognition, neural nets, and machine learning were applied to four real-world data sets. Each of these data sets has been previously analyzed and reported in the statistical, medical, or machine learning literature. The data sets are characterized by statistical uncertainty; there is no completely accurate solution to these problems. Training and testing or resampling techniques are used to estimate the true error rates of classification methods. Detailed attention is given to the analysis of performance of the neural nets using back propagation. For these problems, which have relatively few hypotheses and features, the machine learning procedures for rule induction or tree induction clearly performed best. Keywords: Expert systems.

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Document Details

Document Type
Technical Report
Publication Date
Feb 28, 1989
Accession Number
ADA206226

Entities

People

  • Casimir A. Kulikowski
  • Sholom M. Weiss

Organizations

  • Rutgers University–New Brunswick

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Computer Science
  • Computers
  • Data Mining
  • Data Sets
  • Expert Systems
  • Information Science
  • Machine Learning
  • Network Science
  • Neural Networks
  • Pattern Recognition
  • Reasoning
  • Recognition
  • Statistical Algorithms
  • Statistics

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks