Affiliations: College of Command Information System, The PLA University of Science and Technology, Nanjing, Jiangsu, China
Correspondence:
[*]
Corresponding author: Dengchao He, College of Command Information System, The PLA University of Science and Technology, Nanjing, Jiangsu 210007, China. Tel.: +86 13913012757; E-mail:hdchao1989@163.com
Abstract: Label noise, which has not been well studied yet, is
present in many machine learning problems and would make negative influence
to both the classifier and feature selection procedure. To address this
issue, we propose a novel mutual information estimator using Parzen window
based on a probabilistic label noise model, which could be robust to
incorrect label samples. Then we utilize the estimator to achieve a robust
feature selection algorithm for label noise. Experimentation is executed
over a toy dataset and eight real world datasets. Results after performing
classification with a kNN classifier reveal that the proposed approach is
sound and able to reduce the influence of label noise effectively and to
improve the performance of feature selection in the presence of label noise.
