Parameters Optimization of Classifier and Feature Selection Based On Improved Artificial Bee Colony Algorithm

This source preferred by Hongnian Yu and Shuang Cang

This data was imported from Scopus:

Authors: Wang, H., Yu, H., Zhang, Q., Cang, S., Liao, W. and Zhu, F.

Journal: International Conference on Advanced Mechatronic Systems, ICAMechS

Pages: 242-247

eISSN: 2325-0690

ISBN: 9781509053469

ISSN: 2325-0682

DOI: 10.1109/ICAMechS.2016.7813454

© 2016 IEEE. The feature subset selection, along with the parameters of classifier significantly influences the classification accuracy. In order to ensure the optimal classification performance, the artificial bee colony (ABC) algorithm is proposed to simultaneously optimize the feature subset and the parameters of support vector machines (SVM), meanwhile for improving the optimizing performance of ABC algorithm, the initialization and scout bee phase are improved. To evaluate the proposed approach, the simulation was executed based on datasets from the UCI database. The effectiveness of the proposed method is confirmed by simulation results.

This data was imported from Web of Science (Lite):

Authors: Wang, H., Yu, H., Zhang, Q., Cang, S., Liao, W., Zhu, F. and IEEE

Journal: 2016 INTERNATIONAL CONFERENCE ON ADVANCED MECHATRONIC SYSTEMS (ICAMECHS)

Pages: 242-247

ISSN: 2325-0682

The data on this page was last updated at 04:46 on November 24, 2017.