Input Variable Selection for Non-Parametric Regression, Classification, and Time Series Modeling.

Abstract

Variable selection is a critical step in constructing statistical regression, pattern classification, or time series models that are capable of optimum generalization performance. Since the project got started in February 1996, we have implemented the prototype K-test as proposed, carried out extensive testing on regression and time series problems, and developed a selection criterion based upon unsupervised clustering methods. The latter can be applied to both regression and classification type problems. Under ONR sponsorship, a number of criterion functions have been devised and tested for developing the variable selection methodologies. The work on this project has been conducted by Hong Pi and John Moody. Since Hong Pi has taken a job in industry, Howard Yang (from Amari's research group in Tokyo) will continue working on the project in place of Hong.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1997
Accession Number
ADA332822

Entities

People

  • John E. Moody

Organizations

  • Office of Naval Research

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Clustering
  • Computer Science
  • Data Sets
  • Elimination
  • Engineering
  • Extrapolation
  • Genetic Algorithms
  • Models
  • Prototypes
  • Reliability
  • Sensitivity
  • Test And Evaluation

Readers

  • Organizational Psychology.
  • Research Science/Academic Research
  • Urban Planning and Geography.