A novel sub-Kmeans based on co-training approach by transforming single-view into multi-view

Abstract

Semi-supervised learning is dedicated to solving the problem of poor model performance caused by the scarcity of labeled samples. Co-training algorithm, as a representative of semi-supervised learning algorithms, through constructing the diversity of classifiers, is used to solve the shortage of label samples that leads to low classifier accuracy. However, traditional Co-training style algorithms have strong constraints: (1) there are multiple views of data, and (2) the randomness of data views is relatively large, which leads to the low stability of the Co-training style algorithms. To optimize and solve the shortcomings of traditional Co-training style algorithms, a novel Co-training method based on sub-Kmeans named Op-FSCO is proposed. The proposed approach uses an optimal subspace construct method to extend Co-training to single-view data, greatly improving the application field of Co-training style algorithm. The clustering subspace is generated by plotting the probability of the correlation between the feature and the mutual information measure between the feature and the class label. For the resulting cluster subspace, the top two are selected to construct different views. Finally, experiments on a synthetic dataset, UCI dataset, and real-world dataset demonstrate that the proposed approach is more effective than other Co-training style algorithms. On the UCI benchmark dataset, the performance of the Op-FSCO algorithm on 15 UCI datasets is significantly better than the traditional Co-training algorithm. And on the cere bral stroke dataset, our approach is 2%–5% higher than that of traditional Co-training style algorithms.