Pattern Recognition with Partly Missing Data.

Abstract

This paper is an experimental comparison of several simple, cheap ways of doing pattern recognition when some data elements are missing (blank). Pattern recognition methods are usually designed to deal with perfect data, but in the real world data elements are often missing due to error, equipment failure, change of plans, etc. Six methods of dealing with blanks are tested on five data sets. Blanks were inserted at random locations into the data sets. A version of the K-nearest-neighbor technique was used to classify the data and evaluate the six methods. Two methods were found to be consistently poor. Four methods were found to be generally good. Suggestions are given for choosing the best method for a particular application. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1978
Accession Number
ADA060736

Entities

People

  • John K. Dixon

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Classification
  • Computer Programs
  • Computers
  • Data Science
  • Data Sets
  • Databases
  • Experimental Design
  • Information Science
  • Information Systems
  • Measurement
  • Military Research
  • Pattern Recognition
  • Probability
  • Radar Pulses
  • Radar Signals
  • Random Number Generators
  • Recognition

Readers

  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference