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Electrostatic Field Classifier for Deficient Data

  • Marcin Budka
  • Bogdan Gabrys
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 57)

Summary

This paper investigates the suitability of recently developed models based on the physical field phenomena for classification of incomplete datasets. An original approach to exploiting incomplete training data with missing features and labels, involving extensive use of electrostatic charge analogy has been proposed. Classification of incomplete patterns has been investigated using a local dimensionality reduction technique, which aims at exploiting all available information rather than trying to estimate the missing values. The performance of all proposed methods has been tested on a number of benchmark datasets for a wide range of missing data scenarios and compared to the performance of some standard techniques.

Keywords

Charge Redistribution Miss Data Problem Incomplete Dataset Reduce Feature Space Incomplete Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Marcin Budka
    • 1
  • Bogdan Gabrys
    • 1
  1. 1.School of Design, Engineering & ComputingComputational Intelligence Research Group, Bournemouth UniversityUnited Kingdom

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