Solving unbalanced problems in similarity learning using SVM ensemble
Peipei Xia, Li Zhang · 2014
Similarity learning is one of the most fundamental notions in machine learning and pattern recognition. In real-world problems, the number of the paired-samples in similarity set is far less than the ones in dissimilarity set. In other word, there is an unbalanced problem in the paired-samples of similarity learning. This paper presents a scheme of SVM ensemble to solve it. In our scheme, we randomly select some of samples to construct paired-samples, not producing all the paired-samples, and introduces multiple classifiers to obtain higher stability and reliability. As a result, the SVM ensemble can effectively decrease the number of paired-samples in similarity learning and solve the unbalanced data learning to some degree. In the experiments, the SVM ensemble is compared with some classic unbalanced learning algorithms. The results on classification tasks show that the SVM ensemble gains better performance.