A Framework for Multi-class Learning in Micro-array Data Analysis

Authors:
Nicoletta Dessì;Barbara Pes
Affiliations:
Dipartimento di Matematica e Informatica, Università degli Studi di Cagliari, Cagliari, Italy 09124;Dipartimento di Matematica e Informatica, Università degli Studi di Cagliari, Cagliari, Italy 09124
Venue:
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
Year:
2009

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Abstract

A large pool of techniques have already been developed for analyzing micro-array datasets but less attention has been paid on multi-class classification problems. In this context, selecting features and quantify classifiers may be hard since only few training examples are available in each single class. This paper demonstrates a framework for multi-class learning that considers learning a classifier within each class independently and grouping all relevant features in a single dataset. Next step, that dataset is presented as input to a classification algorithm that learns a global classifier across the classes. We analyze two micro-array datasets using the proposed framework. Results demonstrate that our approach is capable of identifying a small number of influential genes within each class while the global classifier across the classes performs better than existing multi-class learning methods.