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KNN-based maximum margin and minimum volume hyper-sphere machine for imbalanced data classification
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DOI：10.1007/s13042-017-0720-6.png)
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Abstract
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Imbalanced data classification is often met in our real life. In this paper, a novel k-nearest neighbor (KNN)-based maximum margin and minimum volume hyper-sphere machine (KNN-(MVHM)-V-3) is presented for the imbalanced data classification. The basic idea is to construct two hyper-spheres with different centres and radiuses. The first one contains majority examples and the second one covers minority examples. When constructing the first hyper-sphere, we remove some redundant majority samples using k-nearest neighbor (KNN)-based strategy to balance two classes of samples. Meanwhile, we maximize the margin between two hyper-spheres and minimize their volumes, which can result in two tight boundaries around each class. Similar to the twin hyper-sphere support vector machine (THSVM), KNN-(MVHM)-V-3 solves two related SVM-type problems and avoids the matrix inverse operation when solving the convex optimization problems. KNN-(MVHM)-V-3 considers not only the within-class information but also the between-class margin, then it achieves better performance in comparison with other state-of-the-art algorithms. Experimental results on twenty-five datasets validate the significant advantages of our proposed algorithm.
Keywords:
Maximum margin
Minimum volume
K-nearest neighbor
Hyper-sphere
KNN-(MVHM)-V-3
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