Batch classification with applications in computer aided diagnosis

Authors:
Volkan Vural;Glenn Fung;Balaji Krishnapuram;Jennifer Dy;Bharat Rao
Affiliations:
Department of Electrical and Computer Engineering, Northeastern University;Computer Aided Diagnosis and Therapy, Siemens Medical Solutions;Computer Aided Diagnosis and Therapy, Siemens Medical Solutions;Department of Electrical and Computer Engineering, Northeastern University;Computer Aided Diagnosis and Therapy, Siemens Medical Solutions
Venue:
ECML'06 Proceedings of the 17th European conference on Machine Learning
Year:
2006

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Abstract

Most classification methods assume that the samples are drawn independently and identically from an unknown data generating distribution, yet this assumption is violated in several real life problems. In order to relax this assumption, we consider the case where batches or groups of samples may have internal correlations, whereas the samples from different batches may be considered to be uncorrelated. Two algorithms are developed to classify all the samples in a batch jointly, one based on a probabilistic analysis and another based on a mathematical programming approach. Experiments on three real-life computer aided diagnosis (CAD) problems demonstrate that the proposed algorithms are significantly more accurate than a naive SVM which ignores the correlations among the samples.