[3G1-GS-2g] 機械学習：分類

[3G1-GS-2g-05] An Effective Constructive Algorithm of Single Decision Tree Preserving Predictive Performance of Ensemble Learning

〇Taiga Yoshikawa¹, Ayako Yamagiwa¹, Tianxiang Yang¹, Masayuki Goto¹ (1. Waseda University)

Keywords:Ensemble Learning, Interpretability, Neural Network, Autoencoder, Decision Tree

Decision tree is an useful model for label classification and has high interpretability. However, the common size of training data prepared for a decision tree could lead to overfitting. Although the ensemble discriminator of decision tree prevents overfitting and earns high predictive accuracy, it will lose interpretability because of generating a large amount of random decision trees. Therefore, if we can learn a single decision tree that has the same predictive performance to the ensemble discriminator, it should be useful for actual application. In this study, we propose a method for learning a single decision tree with high accuracy with Autoencoder as a generative model, and we also use SMOTE as oversampling method to generate additional learning data by following the distribution of the target data with a small amount of computation. Finally, we show the effectiveness of the proposed method by actual data.