Combining labeled and unlabeled data with co-training
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Semi-supervised learning techniques, such as co-training paradigms, are proposed to deal with data sets with only a few labeled examples. However, the family of co-training paradigms, such as Tri-training and Co-Forest, is likely to mislabel an unlabeled example, thus downgrading the final performance. In practical applications, the labeling process is not always free of error due to subjective reasons. Even some mislabeled examples exist in the few labeled examples given. Supervised clustering provides many benefits in data mining research, but it is generally ineffective with only a few labeled examples. In this paper, a Classification algorithm based on Local Cluster Centers (CLCC) for data sets with a few labeled training data, is proposed. This can reduce the interference of mislabeled data, including those provided by both domain experts and co-training paradigm algorithms. The experimental results on UCI data sets show that CLCC achieves competitive classification accuracy as compared to other traditional and state-of-the-art algorithms, such as SMO, AdaBoost, RandomTree, RandomForest, and Co-Forest.