Abstract
AutoML aims to select an appropriate classification algorithm and corresponding hyperparameters for an individual dataset. However, existing AutoML methods usually ignore the intrinsic imbalance nature of most real-world datasets and lead to poor performance. For handling imbalanced data, sampling methods have been widely used since their independence of the used algorithms. We propose a method named AutoIDL for selecting the sampling methods as well as classification algorithms simultaneously. Particularly, AutoIDL firstly represents datasets as graphs and extracts their meta-features with a graph embedding method. In addition, meta-targets are identified as pairs of sampling methods and classification algorithms for each imbalanced dataset. Secondly, the user-based collaborative filtering method is employed to train a ranking model based on the meta repository to select appropriate sampling methods and algorithms for a new dataset. Extensive experimental results demonstrate that AutoIDL is effective for automated imbalanced data learning and it outperforms the state-of-the-art AutoML methods.
Supported by National Natural Science Foundation of China under Grant No. 61702405 and the China Postdoctoral Science Foundation under Grant No. 2017M623176.
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Zhang, J., Sun, Z., Qi, Y. (2020). AutoIDL: Automated Imbalanced Data Learning via Collaborative Filtering. In: Li, G., Shen, H., Yuan, Y., Wang, X., Liu, H., Zhao, X. (eds) Knowledge Science, Engineering and Management. KSEM 2020. Lecture Notes in Computer Science(), vol 12275. Springer, Cham. https://doi.org/10.1007/978-3-030-55393-7_9
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