Meta-conformity approach to reliable classification

E. N. Smirnov*, G. I. Nalbantov, A. M. Kaptein

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Web of Science)

Abstract

The conformity framework has recently been proposed for the task of reliable classification. Given a classifier B, the framework allows to obtain p-values of the classifications assigned to individual instances. However, applying the framework is a difficult problem: we need to construct an instance non-conformity function for the classifier B. To avoid constructing such a function we propose a meta-conformity approach.(1) If a conformity-based classifier M is available, the approach is to train M as a meta classifier that predicts the correctness of each classification of the classifier B. In this way the classification p-values of the classifier B are represented by the classification p-values of the classifier M.

The meta-conformity approach can be used for constructing classifiers with predefined generalization performance. Experiments show that the approach results in classifiers that can outperform existing conformity-based classifiers.

Original languageEnglish
Pages (from-to)901-915
Number of pages15
JournalIntelligent Data Analysis
Volume13
Issue number6
DOIs
Publication statusPublished - 2009

Keywords

  • Reliable classification
  • conformity framework
  • meta classification

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