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A multi-instance learning algorithm based on nonparallel classifier
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DOI：10.1016/j.amc.2014.05.016.png)
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Abstract
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In this paper, we proposed a new Multiple-Instance Learning (MIL) method based on nonparallel classifier (called MI-NSVM). The method is mainly divided into two steps. The first step is to generate a spare hyperplane and estimate the score of each instance in positive bags. For the second step, MI-NSVM seeks the most positive instance of each positive bag by the information obtained in the first step, and then generates the second hyperplane. MI-NSVM is a useful extension of twin SVM and has the same advantages as it. All experiments show that our method is superior to the traditional MI-SVM and MI-TSVM in both computation time and classification accuracy. (C) 2014 Elsevier Inc. All rights reserved.
Keywords:
Data mining
Multi-instance learning
SVM
Machine learning
Nonparallel classifier
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