mlsanityData to 7 Oct 2026

Efficient Model Selection for Predictive Pattern Mining Model by Safe Pattern Pruning

arXiv 2306.13561 ↗First posted 23 Jun 2023stat.ML cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 28 of its 63 references that are in the map. First posted on arXiv 23 Jun 2023.

Builds on

28 of 63 references.

References · 28

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16 more
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Citing papers per year: 2024: 2.

2 papers.

  1. 2024Distributionally Robust Safe Screeningcited by 1
  2. 2024Distributionally Robust Safe Sample Elimination under Covariate Shiftcited by 0