Building Decision Tree for Imbalanced Classification via Deep Reinforcement Learning

Guixuan Wen, Kaigui Wu
Proceedings of The 13th Asian Conference on Machine Learning, PMLR 157:1645-1659, 2021.

Abstract

Data imbalance is prevalent in classification problems and tends to bias the classifier towards the majority of classes. This paper proposes a decision tree building method for imbalanced binary classification via deep reinforcement learning. First, the decision tree building process is regarded as a multi-step game and modeled as a Markov decision process. Then, the tree-based convolution is applied to extract state vectors from the tree structure, and each node is abstracted into a parameterized action. Next, the reward function is designed based on a range of evaluation metrics of imbalanced classification. Finally, a popular deep reinforcement learning algorithm called Multi-Pass DQN is employed to find an optimal decision tree building policy. The experiments on more than 15 imbalanced data sets indicate that our method outperforms the state-of-the-art methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v157-wen21a, title = {Building Decision Tree for Imbalanced Classification via Deep Reinforcement Learning}, author = {Wen, Guixuan and Wu, Kaigui}, booktitle = {Proceedings of The 13th Asian Conference on Machine Learning}, pages = {1645--1659}, year = {2021}, editor = {Balasubramanian, Vineeth N. and Tsang, Ivor}, volume = {157}, series = {Proceedings of Machine Learning Research}, month = {17--19 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v157/wen21a/wen21a.pdf}, url = {https://proceedings.mlr.press/v157/wen21a.html}, abstract = {Data imbalance is prevalent in classification problems and tends to bias the classifier towards the majority of classes. This paper proposes a decision tree building method for imbalanced binary classification via deep reinforcement learning. First, the decision tree building process is regarded as a multi-step game and modeled as a Markov decision process. Then, the tree-based convolution is applied to extract state vectors from the tree structure, and each node is abstracted into a parameterized action. Next, the reward function is designed based on a range of evaluation metrics of imbalanced classification. Finally, a popular deep reinforcement learning algorithm called Multi-Pass DQN is employed to find an optimal decision tree building policy. The experiments on more than 15 imbalanced data sets indicate that our method outperforms the state-of-the-art methods.} }
Endnote
%0 Conference Paper %T Building Decision Tree for Imbalanced Classification via Deep Reinforcement Learning %A Guixuan Wen %A Kaigui Wu %B Proceedings of The 13th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Vineeth N. Balasubramanian %E Ivor Tsang %F pmlr-v157-wen21a %I PMLR %P 1645--1659 %U https://proceedings.mlr.press/v157/wen21a.html %V 157 %X Data imbalance is prevalent in classification problems and tends to bias the classifier towards the majority of classes. This paper proposes a decision tree building method for imbalanced binary classification via deep reinforcement learning. First, the decision tree building process is regarded as a multi-step game and modeled as a Markov decision process. Then, the tree-based convolution is applied to extract state vectors from the tree structure, and each node is abstracted into a parameterized action. Next, the reward function is designed based on a range of evaluation metrics of imbalanced classification. Finally, a popular deep reinforcement learning algorithm called Multi-Pass DQN is employed to find an optimal decision tree building policy. The experiments on more than 15 imbalanced data sets indicate that our method outperforms the state-of-the-art methods.
APA
Wen, G. & Wu, K.. (2021). Building Decision Tree for Imbalanced Classification via Deep Reinforcement Learning. Proceedings of The 13th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 157:1645-1659 Available from https://proceedings.mlr.press/v157/wen21a.html.

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