Adaptive feature split selection for co-training: Application to tire irregular wear classification

Co-training is a practical and powerful semi-supervised learning method. It yields high classification accuracy with a training data set containing only a small set of labeled data. Successful performance in co-training requires two important conditions on the features: diversity and sufficiency. In this paper, we propose a novel mutual information (MI) based approach inspired by the idea of dependent component analysis (DCA) to achieve feature splits that are maximally independent between-subsets (diversity) or within-subsets (sufficiency). We evaluate the relationship between the classification performance and the relative importance of the two conditions. Experimental results on actual tire data indicate that compared to diversity, sufficiency has a more significant impact on their classification accuracy. Further results show that co-training with feature splits obtained by the MI-based approach yields higher accuracy than supervised classification and significantly higher when using a small set of labeled training data.