SELECTION OF DECISION BOUNDARIES FOR LOGISTIC REGRESSION

• 松井 秀俊

  九州大学数理学研究院

抄録

We propose a method that selects decision boundaries for the logistic regression model by applying sparse regularization. We can investigate which decision boundaries are truly necessary for the multinomial logistic regression model by letting some of the coefficient parameters or the differences between them approach zero. The model is estimated by the maximum penalized likelihood method with a fused lasso-type penalty. We also introduce various model selection criteria for evaluating models estimated by the penalized likelihood method. Simulation studies are conducted in order to evaluate the effectiveness of the proposed method. Real data analysis provides new insights into how each of the predictors contributes to the classification.

収録刊行物

• Bulletin of informatics and cybernetics

  Bulletin of informatics and cybernetics 47 83-95, 2015-12

  統計科学研究会

キーワード

• Classification
• Fused lasso
• Logistic regression model
• Model selection
• Regularization

詳細情報

• CRID: 1390572174802557696
• NII論文ID: 120006401435
• NII書誌ID: AA10634475
• DOI: 10.5109/1909526
• ISSN: 2435743X
• ISSN: 0286522X
• HANDLE: 2324/1909526
• 本文言語コード: en
• データソース種別:
  • JaLC
  • IRDB
  • Crossref
  • CiNii Articles
• 抄録ライセンスフラグ: 使用可