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Multi-objective Evolutionary Instance Selection for Multi-label Classification

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13629)


Multi-label classification is an important topic in machine learning, where each instance can be classified into more than one category, i.e., have a subset of labels instead of only one. Among existing methods, ML-kNN [25], the direct extension of k-nearest neighbors algorithm to the multi-label scenario, has received much attention due to its conciseness, great interpretability, and good performance. However, ML-kNN usually suffers from a terrible storage cost since all training instances need to be saved in the memory. To address this issue, a natural way is instance selection, intending to save the important instances while deleting the redundant ones. However, previous instance selection methods mainly focus on the single-label scenario, which may have a poor performance when adapted to the multi-label scenario. Recently, few works begin to consider the multi-label scenario, but their performance is limited due to the inapposite modeling. In this paper, we propose to formulate the instance selection problem for ML-kNN as a natural bi-objective optimization problem that considers the accuracy and the number of retained instances simultaneously, and adapt NSGA-II to solve it. Experiments on six real-world data sets show that our proposed method can achieve both not worse prediction accuracy and significantly better compression ratio, compared with state-of-the-art methods.


  • Multi-label classification
  • ML-kNN
  • Instance selection
  • Multi-objective optimization
  • Multi-objective evolutionary algorithm

C. Qian—This work was supported by the National Science Foundation of China (62022039, 62106098).

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Liu, D., Shang, H., Hong, W., Qian, C. (2022). Multi-objective Evolutionary Instance Selection for Multi-label Classification. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13629. Springer, Cham.

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