Title :
Combination of multiple classifiers using local accuracy estimates
Author :
Woods, Kevin ; Kegelmeyer, W. Philip, Jr. ; Bowyer, Kevin
Author_Institution :
Dept. of Comput. Sci. & Eng., Univ. of South Florida, Tampa, FL, USA
fDate :
4/1/1997 12:00:00 AM
Abstract :
This paper presents a method for combining classifiers that uses estimates of each individual classifier´s local accuracy in small regions of feature space surrounding an unknown test sample. An empirical evaluation using five real data sets confirms the validity of our approach compared to some other combination of multiple classifiers algorithms. We also suggest a methodology for determining the best mix of individual classifiers
Keywords :
feature extraction; optimisation; pattern classification; classifier fusion; dynamic classifier selection; feature space; local accuracy estimates; multiple classifiers; pattern recognition; receiver operating characteristic; Algorithm design and analysis; Handwriting recognition; Logistics; Partitioning algorithms; Testing; Training data; Voting;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
