Ensemble Feature election with the Simple Bayesian Classification in Medical Diagnostics

Authors:
Alexey Tsymbal;Seppo Puuronen
Affiliations:
-;-
Venue:
CBMS '02 Proceedings of the 15th IEEE Symposium on Computer-Based Medical Systems (CBMS'02)
Year:
2002

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Abstract

Ensembles of simple Bayesian classifiers have traditionally not been in the focus ofclassification research partly because of the stability of simple Bayesian classifier and becauseof the rarely valid basic assumption that the classification features are independent of eachother,given the predicted value.As a way to try to circumvent these problems we suggest theuse of an ensemble of simple Bayesian classifiers each concentrating on solving a sub-problem of the problem domain.Our experiments with the problem of separating acute appendicitis show that in this way it is possible to retain the comprehensibility and at the same time to increase the diagnostic accuracy,sensitivity,and specificity.The advantages of the approach include also simplicity and speed of learning,small storage space needed during theclassification,speed of classification,and the possibility of incremental learning.