TY - GEN
T1 - Adaptive feature redundancy minimization
AU - Zhang, Rui
AU - Tong, Hanghang
AU - Hu, Yifan
N1 - Funding Information:
This work is supported by NSF (IIS-1651203, IIS-1715385), and DHS (2017-ST-061-QA0001).
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/11/3
Y1 - 2019/11/3
N2 - 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.
AB - 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.
KW - Adaptive learning
KW - Feature redundancy
UR - http://www.scopus.com/inward/record.url?scp=85075457430&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075457430&partnerID=8YFLogxK
U2 - 10.1145/3357384.3358112
DO - 10.1145/3357384.3358112
M3 - Conference contribution
AN - SCOPUS:85075457430
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 2417
EP - 2420
BT - CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 28th ACM International Conference on Information and Knowledge Management, CIKM 2019
Y2 - 3 November 2019 through 7 November 2019
ER -