publication . Part of book or chapter of book . 2010

Attribute Interactions in Medical Data Analysis

Blaž Zupan; Blaž Zupan; Ivan Bratko; Dragica Smrke; Aleks Jakulin; Janez Demšar;

Open Access

Published: 25 Jun 2010

Publisher: Springer Berlin Heidelberg

Summary

Abstract

There is much empirical evidence about the success of naive Bayesian classification (NBC) in medical applications of attribute-based machine learning. NBC assumes conditional independence between attributes. In classification, such classifiers sum up the pieces of class-related evidence from individual attributes, independently of other attributes. The performance, however, deteriorates significantly when the “interactions” between attributes become critical. We propose an approach to handling attribute interactions within the framework of “voting” classifiers, such as NBC. We propose an operational test for detecting interactions in learning data and a procedur...

doi: 10.1007/978-3-540-39907-0_32

Subjects

free text keywords: Naive Bayes classifier, Empirical evidence, Conditional independence, Voting, media_common.quotation_subject, media_common, Subject-matter expert, Data mining, computer.software_genre, computer, Machine learning, Classifier (linguistics), Information and Computer Science, Structuring, Computer science, Artificial intelligence, business.industry, business

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