Combining Multiple Implicit-Explicit Interactions for Regression Analysis

초록

Regression analysis is one of the most widely used data analysis methods, and it is increasingly important to obtain accurate results from it. To obtain accurate prediction results of regression analysis through machine learning, we must select appropriate features and train various feature interactions. The combinatorial model consists of a combination of various subordinate components and is used for automatic training of various feature interactions. However, existing combinatorial models are inefficient because they can train only limited feature interactions and must combine several components. To overcome these limitations, this study proposes a new model called eXtreme Interaction Network (XIN). XIN can automatically learn various explicit interactions, various levels of implicit higher-order interactions, and polynomial features. We compared the proposed XIN with existing models using four datasets with different characteristics to demonstrate that the proposed model has higher performance and lower or comparable time and space complexities. Furthermore, we conducted experiments while changing the various hyper-parameters of the XIN and demonstrated the improved performance of the proposed method in various environments.

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