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Entropy 2018, 20(4), 245;

Multi-Graph Multi-Label Learning Based on Entropy

College of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Author to whom correspondence should be addressed.
Received: 25 March 2018 / Revised: 30 March 2018 / Accepted: 30 March 2018 / Published: 2 April 2018
(This article belongs to the Special Issue Entropy-based Data Mining)
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Recently, Multi-Graph Learning was proposed as the extension of Multi-Instance Learning and has achieved some successes. However, to the best of our knowledge, currently, there is no study working on Multi-Graph Multi-Label Learning, where each object is represented as a bag containing a number of graphs and each bag is marked with multiple class labels. It is an interesting problem existing in many applications, such as image classification, medicinal analysis and so on. In this paper, we propose an innovate algorithm to address the problem. Firstly, it uses more precise structures, multiple Graphs, instead of Instances to represent an image so that the classification accuracy could be improved. Then, it uses multiple labels as the output to eliminate the semantic ambiguity of the image. Furthermore, it calculates the entropy to mine the informative subgraphs instead of just mining the frequent subgraphs, which enables selecting the more accurate features for the classification. Lastly, since the current algorithms cannot directly deal with graph-structures, we degenerate the Multi-Graph Multi-Label Learning into the Multi-Instance Multi-Label Learning in order to solve it by MIML-ELM (Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine). The performance study shows that our algorithm outperforms the competitors in terms of both effectiveness and efficiency. View Full-Text
Keywords: multi-graph multi-label; entropy; informative subgraphs; extreme learning machine multi-graph multi-label; entropy; informative subgraphs; extreme learning machine

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Zhu, Z.; Zhao, Y. Multi-Graph Multi-Label Learning Based on Entropy. Entropy 2018, 20, 245.

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