Abstract
ML-kNN is a well-known algorithm for multi-label classification, but it just assumes the independence of labels and instances. In fact, in the real world, labels or instances are more or less related via explicit or implicit relationships. In this paper, we propose an improved ML-kNN approach that takes the coupled similarity of attributes and labels into account, where coupling between attributes is used to find k nearest neighbors for instances and coupling between labels is used to predict the labels of unseen instances. Experimental results show that our proposed method outperforms the traditional ML-kNN.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China under Grant No. 61262069, No. 61472346, the Natural Science Foundation of Yunnan Province under Grant No. 2015FB114, No. 2015FB149, No. 2016FA026, Program for Young and Middle-aged Skeleton Teachers, Yunnan University, and Program for Innovation Research Team in Yunnan University under Grant No. XT412011.
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Yang, X., Zhou, L., Wang, L. (2016). An Improved ML-kNN Approach Based on Coupled Similarity. In: Morishima, A., et al. Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9865. Springer, Cham. https://doi.org/10.1007/978-3-319-45835-9_7
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DOI: https://doi.org/10.1007/978-3-319-45835-9_7
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