[2L5-GS-3] Knowledge utilization and sharing

[2L5-GS-3-02] Sequential Pattern Extraction with Constrained Decision Tree

〇Tomu Yanabe¹, Ryo Adachi¹ (1. DeNA Co.,Ltd.)

Keywords:Machine Learning, Pattern Extraction, Decision Tree, Time Series Data

There is a great need for extracting the sequential patterns from data that represent the sequence of human decision making, such as purchase histories and game action histories, as it can lead to measures for improving various services. Decision trees, which are representative identification models in the machine learning field, are highly interpretable models that allow us to find the basis of decisions from the splitting node features of the learned models. On the other hand, decision trees do not retain time-sequential order relations in their learning, and it is difficult to extract a sequential pattern from them. On the other hand, models that repeat recursive input such as RNN can be trained while preserving the order relation of series data, but the interpretability of the trained model is low. Therefore, in this study, we propose a model that preserves the order relation of series data by adding a series constraint to the search range of splitting node features when training decision trees. Since this model preserves the series order of the features used in the branching process, it is possible to extract interpretable sequential patterns and important branches of decision making in the series from the trained decision tree. In this paper, we conduct experiments on real data and show the effectiveness of the model based on the extracted patterns.