TY - GEN
T1 - Feature selection based on improved runner-root algorithm using chaotic singer map and opposition-based learning
AU - Ibrahim, Rehab Ali
AU - Oliva, Diego
AU - Ewees, Ahmed A.
AU - Lu, Songfeng
PY - 2017
Y1 - 2017
N2 - The feature selection (FS) is an important step for data analysis. FS is used to reduce the dimension of data by selecting the relevant features; while removing the redundant, noisy and irrelevant features that lead to degradation of the performance. Several swarm techniques are used to solve the FS problem and these methods provide results better than classical approaches. However, most of these techniques have limitations such as slow convergence and time complexity. These limitations occur due that all the agents update their position according to the best one. However, this best agent may be not the optimal global solution for FS, therefore, the swarm getting stuck in a local solution. This paper proposes an improved Runner-Root Algorithm (RRA). The RRA is combined with chaotic Singer map and opposition-based learning to increase its accuracy. The experiments are performed in eight datasets and the performance of the proposed method is compared against swarm algorithms.
AB - The feature selection (FS) is an important step for data analysis. FS is used to reduce the dimension of data by selecting the relevant features; while removing the redundant, noisy and irrelevant features that lead to degradation of the performance. Several swarm techniques are used to solve the FS problem and these methods provide results better than classical approaches. However, most of these techniques have limitations such as slow convergence and time complexity. These limitations occur due that all the agents update their position according to the best one. However, this best agent may be not the optimal global solution for FS, therefore, the swarm getting stuck in a local solution. This paper proposes an improved Runner-Root Algorithm (RRA). The RRA is combined with chaotic Singer map and opposition-based learning to increase its accuracy. The experiments are performed in eight datasets and the performance of the proposed method is compared against swarm algorithms.
KW - Chaotic map
KW - Feature selection (FS)
KW - Metaheuristic algorithms (MH)
KW - Opposition-based learning (OBL)
KW - Runner-Root Algorithm (RRA)
KW - Swarm intelligence (SI)
UR - http://www.scopus.com/inward/record.url?scp=85035149648&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85035149648&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-70139-4_16
DO - 10.1007/978-3-319-70139-4_16
M3 - Conference contribution
AN - SCOPUS:85035149648
SN - 9783319701387
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 156
EP - 166
BT - Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
A2 - Zhao, Dongbin
A2 - Li, Yuanqing
A2 - El-Alfy, El-Sayed M.
A2 - Liu, Derong
A2 - Xie, Shengli
PB - Springer Verlag
T2 - 24th International Conference on Neural Information Processing, ICONIP 2017
Y2 - 14 November 2017 through 18 November 2017
ER -