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An active multi-class classification using privileged information and belief function
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DOI：10.1007/s13042-019-00991-w.png)
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Abstract
En 中文
Many classification models, based on support vector machine, have been designed so far to improve classification performance in both supervised and semi-supervised learning. One of the studies which is done in this case is about the use of privileged information that is hidden in training data. However, the challenge is how to find the privileged information. In most researches, experts have defined privileged information, but in this paper, it has been tried to automatically select a feature as privileged information and classify training data into several groups. This grouping has been used to correct the decision function of classifier. Moreover, the proposed classifier has been used in one-against-all (OAA) approach for semi-supervised datasets. To overcome uncertain areas in OAA, belief function and active learning techniques are applied to extract the most informative samples. The experimental results indicate the superiority of the proposed method among the other state-of-the-art methods in terms of classification accuracy.
Keywords:
Classification
Support vector machine
Privileged information
Active learning
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