Abstract
In multi-label classification, each sample can be associated with a set of class labels. When the number of labels grows to the hundreds or even thousands, existing multi-label classification methods often become computationally inefficient. To this end, dimensionality reduction strategy is applied to label space via exploiting label correlation information, resulting in label embedding and label selection techniques. Compared with a lot of label embedding work, less attention has been paid to label selection techniques due to its difficulty. Therefore, it is a challenging task to design more effective label selection techniques for multi-label classification. Column subset selection is the problem of selecting a small portion of columns from a large data matrix as one form of interpretable data summarization. So, the column subset selection problem translates naturally to this purpose, as it provides simple linear models for low-rank data reconstruction. Iterative column subset selection is one of the methods to solve the problem of column subset selection, and this method can achieve a good result in the problem. In this paper, we first execute iterative column subset selection to select a small portion of columns from a large label matrix, in the prediction stage, we do some processing on the recovery matrix. So, a new method of multi-label classifier based on iterative column subset selection is proposed. The new method is tested on six publicly available datasets with varying numbers of labels. The experimental evaluation shows that the new method works particularly well on datasets with a large number of labels.
Supported by the Natural Science Foundation of China (NSFC) under grants 62076134 and 61703096.
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The following is the detailed process of Iterative column subset selection algorithm.
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Peng, T., Li, J., Xu, J. (2022). Label Selection Algorithm Based on Iteration Column Subset Selection for Multi-label Classification. In: Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2022. Lecture Notes in Computer Science, vol 13426. Springer, Cham. https://doi.org/10.1007/978-3-031-12423-5_22
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