Feature selection for binary data: medical diagnosis with fuzzy sets

Authors:
James C. Bezdek
Affiliations:
Marquette University, Milwaukee, Wisconsin
Venue:
AFIPS '76 Proceedings of the June 7-10, 1976, national computer conference and exposition
Year:
1976

Citing 3
Cited 1

Fuzzy sets and pattern recognition

Fuzzy sets and pattern recognition
An Algorithm for Detecting Unimodal Fuzzy Sets and Its Application as a Clustering Technique

IEEE Transactions on Computers
Optimal Fuzzy Partitions: A Heuristic for Estimating the Parameters in a Mixture of Normal Distributions

IEEE Transactions on Computers

A robust approach for multivariate binary vectors clustering and feature selection

ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

The notion of fuzzy sets---sets with imprecise boundaries---is a natural cornerstone upon which to build algorithms based on approximate reasoning. Since their inception in 1965 by Zadeh, fuzzy sets have led to first steps towards quantifying data analysis in many fields previously immune to mathematical examination. A fairly exhaustive introduction to the theory and applications of fuzzy sets lists 238 papers dealing with a great variety of recent investigations.