Optimizing between data transformation and parametric weighting for stable binary classification

Kangrok Oh, Zhengguo Li, Beom Seok Oh, Kar Ann Toh

Research output: Contribution to journalArticle

Abstract

In this paper, an optimization problem is formulated for stable binary classification. Essentially, the objective function seeks to optimize a full data transformation matrix along with the learning of a linear parametric model. The data transformation matrix and the weight parameter vector are alternatingly optimized based on the area above the receiver operating characteristic curve criterion. The proposed method improves the existing means via an optimal data transformation rather than that based on the diagonal, random and ad-hoc settings. This optimal transformation stretches beyond the fixed settings of known optimization methods. Extensive experiments using 34 binary classification data sets show that the proposed method can be more stable than competing classifiers. Specifically, the proposed method shows robustness to imbalanced and small training data sizes in terms of classification accuracy with statistical evidence.

Original languageEnglish
Pages (from-to)1614-1637
Number of pages24
JournalJournal of the Franklin Institute
Volume355
Issue number4
DOIs
Publication statusPublished - 2018 Mar

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Optimizing between data transformation and parametric weighting for stable binary classification'. Together they form a unique fingerprint.

  • Cite this