Co-Training Combining Sample Synthesis with Local Density and Inter-Class Interpolation
The co-training algorithm addresses the limitations of single-view learning by utilizing multi-view features to train two classifiers that complement each other and enhance learning performance.However,the lack of labeled samples and the problem of unlabeled samples not being effectively utilized restrict the further improvement of the performance of the co-training algorithm.In order to solve the above problems,a co-training algorithm combining local density sample synthesis and inter-class interpolation was presented.The algorithm first utilizes the local density sample synthesis method to expand the labeled sample set to improve the spatial structure of the data and obtain two initial classifiers with better performance.Then,the K-means clustering algorithm is used to cluster the unlabeled samples with inconsistent categories predicted by the two classifiers,and the samples of different categories are randomly selected to interpolate to generate new samples.These new samples are then added to each classifier,which pushes the decision boundary further from the class boundary to achieve a larger margin.This process is repeated until the final classifiers are obtained.Experimental results on twelve UCI datasets verify the effectiveness of the proposed algorithm.
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