A Study of a Procedure for Reducing the Feature Set of Workload Data.

Abstract

Workload models are extremely important for computer performance evaluation. The problem of feature reduction for the purpose of the formulation of workload models has received widespread attention. This paper briefly reviews existing schemes for feature selection and reduction, and proposes a feature reduction algorithm based on a linear decision-tree classifier. An example is presented to illustrate the use and validity of this algorithm. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1980
Accession Number
ADA083727

Entities

People

  • Herman D. Hughes

Organizations

  • Michigan State University

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Automata
  • Clustering
  • Computational Complexity
  • Computers
  • Data Science
  • Data Sets
  • Eigenvalues
  • Feature Selection
  • Machine Learning
  • Michigan
  • Monitoring
  • Pattern Recognition
  • Recognition
  • Test And Evaluation
  • Workload

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Computer Vision.
  • Parallel and Distributed Computing.