A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining classifiers in error correcting output coding
Systems and Computers in Japan
Discriminative sparse representations for cervigram image segmentation
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
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In this paper, we introduce a new classifier ensemble approach, applied to tissue segmentation in optical images of the uterine cervix. Ensemble methods combine the predictions of a set of diverse classifiers. The main contribution of our approach is an effective way of combination based on each classifier's performance level-namely, the sensitivity p and specificity q, which also produces an optimal estimate of the true segmentation. In comparison with previous work [1] that utilizes the STAPLE algorithm [2] for performance level based combination, this work achieves multiple-observer segmentation in a Bayesian decision framework using the maximum a posterior (MAP) principle, considering each classifier as an observer. In our experiments, we applied our method and several other popular ensemble methods to the problem of detecting Acetowhite regions in cervical images. On 100 images, the overall performance of the proposed method is better than: (i) an overall classifier learned using the entire training set, (ii) average voting ensemble, (iii) ensemble based on the STAPLE algorithm; it is comparable to that of majority voting and that of the (manually picked) best-performing individual classifier in the ensemble set.