Building Classifiers with Independency Constraints

被引：265

作者：

Calders, Toon ^{[1
]}

Kamiran, Faisal ^{[1
]}

Pechenizkiy, Mykola ^{[1
]}

机构：

[1] Eindhoven Univ Technol, NL-5600 MB Eindhoven, Netherlands

来源：

2009 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2009) | 2009年

关键词：

D O I：

10.1109/ICDMW.2009.83

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper we study the problem of classifier learning where the input data contains unjustified dependencies between some data attributes and the class label. Such cases arise for example when the training data is collected from different sources with different labeling criteria or when the data is generated by a biased decision process. When a classifier is trained directly on such data, these undesirable dependencies will carry over to the classifier's predictions. In order to tackle this problem, we study the classification with independency constraints problem: find an accurate model for which the predictions are independent from a given binary attribute. We propose two solutions for this problem and present an empirical validation.

引用

页码：13 / 18

页数：6