A Logical and Probabilistic Technique for Classification and Dimensionality Reduction for Objects with Categorical Data

Abstract

A supervised learning technique, the Attribute Importance Measure (AIM) method, is proposed for the classification of objects with categorical attributes. The advantage of this method over existing techniques is its ability to perform classification and dimensionality reduction, or feature selection, with the same algorithm. The method uses probabilistic measures alongside logical concepts of sufficiency, necessity and irrelevance in providing corresponding weights to values in attribute value pairs. Finally an efficient search algorithm is developed which generates decision rules for classification. The performance of the new method is demonstrated on a commonly used machine learning data set.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2004
Accession Number
ADA427562

Entities

People

  • Mark Porter

Organizations

  • Defence Science and Technology Group

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Australia
  • Classification
  • Computational Science
  • Computer Science
  • Data Mining
  • Data Sets
  • Databases
  • Dimensionality Reduction
  • Feature Selection
  • Information Science
  • Information Systems
  • Machine Learning
  • Probability
  • Supervised Machine Learning
  • Universities

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Computer Vision.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks