Efficient Model Selection for Predictive Pattern Mining Model by Safe
  Pattern Pruning
Takumi Yoshida Hiroyuki Hanada Kazuya Nakagawa Kouichi Taji Koji Tsuda Ichiro Takeuchi
Predictive pattern mining is an approach used to construct prediction models
when the input is represented by structured data, such as sets, graphs, and
sequences. The main idea behind predictive pattern mining is to build a
prediction model by considering substructures, such as subsets, subgraphs, and
subsequences (referred to as patterns), present in the structured data as
features of the model. The primary challenge in predictive pattern mining lies
in the exponential growth of the number of patterns with the complexity of the
structured data. In this study, we propose the Safe Pattern Pruning (SPP)
method to address the explosion of pattern numbers in predictive pattern
mining. We also discuss how it can be effectively employed throughout the
entire model building process in practical data analysis. To demonstrate the
effectiveness of the proposed method, we conduct numerical experiments on
regression and classification problems involving sets, graphs, and sequences.