Research Of Two Class Confidence Classification Based On One Class Classifier

Fangchun, Jiang; Shengfeng, Tian

doi:10.2478/cait-2014-0041

Abstract

To have simple and efficient confidence machine learning is an important focus in confidence machine researches. Using one class classifier as a tool, the paper applies it twice for two-class classification problems. Setting reject options and a multi-layer ensemble learning method are used in this study. In this method there is no necessity to set up a specific threshold and the confidence computation is omitted. Realizing five experiments, the study proves it as efficient.

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Research Of Two Class Confidence Classification Based On One Class Classifier

Abstract

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