H. Shu and R. Y. -N. Wong, "Modified Binary Ant Colony Optimization for Drift Compensation," 2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA), 2021, pp. 1131-1135, doi: 10.1109/ICIEA51954.2021.9516232.
Abstract:
In data analytics and pattern recognition, feature selection is a critical task to provide a subset of features with minimum redundancy. This reduces the computation time as well as cost. In this manuscript, a correlation based feature selection approach based on a modified binary ant colony optimization algorithm (MBACO) is proposed. Combined with random forest regression, the proposed MBACO algorithm is customized for a drift compensation application. In this application, the ant road map is initialized to avoid the local optimum. The proposed method is compared with that of binary particle swarm optimization on a well-known UCI dataset. Experimental results show that the proposed method exhibits better performance over the binary particle swarm optimization based approach.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Singapore Ministry of National Development and the National Research Foundation - Land and Liveability National Innovation Challenge (L2 NIC) Research Programme
Grant Reference no. : L2NICTDF1-2017-3