Abstract
In the multi-instance multi-label learning framework, an example is described by multiple instances and associated with multiple class labels at the same time. An idea of tackling with multi-instance multi-label problems is to identify its equivalence in the traditional supervised learning framework. However, some useful information such as the correlation between labels may be lost in the process of degeneration, which will influence the classification performance. In E-MIMLSVM+ algorithm, multi-task learning techniques are utilized to incorporate label correlations, while it is time consuming as well as memory consuming. Therefore, we propose an improved algorithm. In our algorithm, the classifier chains method is applied in E-MIMLSVM+ to incorporate label correlations instead of multi-task learning techniques. The experimental results show that the proposed algorithm can reduce time complexity and improve the predictive performance.
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Acknowledgments
This paper is supported by the Fundamental Research Funds for the Central Universities (No. R1407008A, 09CX04031A). The authors are grateful for the anonymous reviewers who made constructive comments.
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Li, C., Zhang, Y. & Lu, L. An MIMLSVM algorithm based on ECC. Appl Intell 42, 537–543 (2015). https://doi.org/10.1007/s10489-014-0608-z
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DOI: https://doi.org/10.1007/s10489-014-0608-z