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 journalArticlepeer-review


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
Issue number4
Publication statusPublished - 2018 Mar

Bibliographical note

Funding Information:
We are thankful to the anonymous reviewers for their constructive comments to improve the manuscript. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Grant number: NRF- 2015R1D1A1A09061316 ).

Publisher Copyright:
© 2017

All Science Journal Classification (ASJC) codes

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


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