EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Interactive optimization for steering machine classification
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
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Many different classification models and techniques have been employed on gene expression data. These computational methods are in rapid and continuous evolution and there is no clear consensus on which methods are best to cope with the complex microarray data analysis. Currently ensembles of classifiers are regarded as one of the best classification techniques as they can achieve excellent classification accuracy in comparison to single classifiers methods. One of their main drawbacks is their incomprehensibility. This paper addresses the important issue of the tradeoff between accuracy and comprehensibility when building ensembles and proposes a novel visual technique for interactive interpretation of the knowledge from the small ensembles consisting of only a few decision trees. This way we can achieve better accuracy compared to single classifier, but still maintain a certain level of comprehensibility in small ensembles. The results show that our small ensembles outperform the single classifiers and still retain comprehensibility. Our study also points out that in order to take advantage of our proposed method we need more effective small ensemble building techniques.