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Efficient model selection for predictive pattern mining model by safe pattern pruning.

Takumi Yoshida1, Hiroyuki Hanada2, Kazuya Nakagawa1

  • 1Department of Engineering, Nagoya Institute of Technology, Nagoya, Aichi 466-8555, Japan.

Patterns (New York, N.Y.)
|December 18, 2023
PubMed
Summary
This summary is machine-generated.

Predictive pattern mining builds models from structured data, but faces challenges with too many patterns. This study introduces a safe pattern pruning method to manage pattern numbers effectively in model building.

Keywords:
convex optimizationgraph miningitem set miningpredictive pattern miningsafe screeningsequence miningsparse learning

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Area of Science:

  • Data Mining and Machine Learning
  • Computational Intelligence
  • Pattern Recognition

Background:

  • Predictive pattern mining constructs models from structured data like sets, graphs, and sequences.
  • It utilizes substructures (patterns) as model features, facing challenges with exponential pattern growth.
  • This growth complicates model building and reduces efficiency.

Purpose of the Study:

  • To propose a novel method for addressing the challenge of excessive pattern numbers in predictive pattern mining.
  • To introduce the safe pattern pruning method for efficient model construction.
  • To demonstrate the method's applicability across various structured data types and machine learning tasks.

Main Methods:

  • Developed a "safe pattern pruning" technique to mitigate the combinatorial explosion of patterns.
  • Integrated the pruning method into the predictive pattern mining workflow.
  • Conducted numerical experiments on regression and classification tasks using set, graph, and sequence data.

Main Results:

  • The proposed safe pattern pruning method effectively reduces the number of patterns.
  • The method demonstrates efficiency in practical data analysis and model building.
  • Successful application in both regression and classification problems across diverse structured data.

Conclusions:

  • Safe pattern pruning is a viable solution to the pattern explosion problem in predictive pattern mining.
  • The method enhances the practicality and efficiency of building predictive models from structured data.
  • This approach offers a significant advancement for machine learning with complex data types.