@inproceedings{70f95c7515144fef8df04e9ca34bff88,
title = "Adaptive feature redundancy minimization",
abstract = "Most existing feature selection methods select the top-ranked features according to certain criterion. However, without considering the redundancy among the features, the selected ones are frequently highly correlated with each other, which is detrimental to the performance. To tackle this problem, we propose a framework regarding adaptive redundancy minimization (ARM) for the feature selection. Unlike other feature selection methods, the proposed model has the following merits: (1) The redundancy matrix is adaptively constructed instead of presetting it as the priori information. (2) The proposed model could pick out the discriminative and non-redundant features via minimizing the global redundancy of the features. (3) ARM can reduce the redundancy of the features from both supervised and unsupervised perspectives.",
keywords = "Adaptive learning, Feature redundancy",
author = "Rui Zhang and Hanghang Tong and Yifan Hu",
note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 28th ACM International Conference on Information and Knowledge Management, CIKM 2019 ; Conference date: 03-11-2019 Through 07-11-2019",
year = "2019",
month = nov,
day = "3",
doi = "10.1145/3357384.3358112",
language = "English (US)",
series = "International Conference on Information and Knowledge Management, Proceedings",
publisher = "Association for Computing Machinery",
pages = "2417--2420",
booktitle = "CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management",
address = "United States",
}