Semi-supervised Classification Based on Clustering Ensembles

Abstract

In many real-world applications, there only exist very few labeled samples, while a large number of unlabeled samples are available. Therefore, it is difficult for some traditional semi-supervised algorithms to generate the useful classifiers to evaluate the labeling confidence of unlabeled samples. In this paper, a new semi-supervised classification based on clustering ensembles named SSCCE is proposed. It takes advantages of clustering ensembles to generate multiple partitions for a given dataset, and then uses the clustering consistency index to determine the labeling confidence of unlabeled samples. The algorithm can overcome some defects about the traditional semi-supervised classification algorithms, and enhance the performance of the hypothesis trained on very few labeled samples by exploiting a large number of unlabeled samples. Experiments carried out on ten public data sets from UCI machine learning repository show that this method is effective and feasible.