PMID- 34002110
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220716
IS  - 1433-7541 (Print)
IS  - 1433-755X (Electronic)
IS  - 1433-7541 (Linking)
VI  - 24
IP  - 3
DP  - 2021
TI  - Improved barnacles mating optimizer algorithm for feature selection and support 
      vector machine optimization.
PG  - 1249-1274
LID - 10.1007/s10044-021-00985-x [doi]
AB  - With the rapid development of computer technology, data collection becomes 
      easier, and data object presents more complex. Data analysis method based on 
      machine learning is an important, active, and multi-disciplinarily research 
      field. Support vector machine (SVM) is one of the most powerful and fast 
      classification models. The main challenges SVM faces are the selection of feature 
      subset and the setting of kernel parameters. To improve the performance of SVM, a 
      metaheuristic algorithm is used to optimize them simultaneously. This paper first 
      proposes a novel classification model called IBMO-SVM, which hybridizes an 
      improved barnacle mating optimizer (IBMO) with SVM. Three strategies, including 
      Gaussian mutation, logistic model, and refraction-learning, are used to improve 
      the performance of BMO from different perspectives. Through 23 classical 
      benchmark functions, the impact of control parameters and the effectiveness of 
      introduced strategies are analyzed. The convergence accuracy and stability are 
      the main gains, and exploration and exploitation phases are more properly 
      balanced. We apply IBMO-SVM to 20 real-world datasets, including 4 extremely 
      high-dimensional datasets. Experimental results are compared with 6 
      state-of-the-art methods in the literature. The final statistical results show 
      that the proposed IBMO-SVM achieves a better performance than the standard 
      BMO-SVM and other compared methods, especially on high-dimensional datasets. In 
      addition, the proposed model also shows significant superiority compared with 4 
      other classifiers.
CI  - (c) The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of 
      Springer Nature 2021.
FAU - Jia, Heming
AU  - Jia H
AD  - College of Information Engineering, Sanming University, Sanming, 365004 China. 
      GRID: grid.440620.4. ISNI: 0000 0004 1799 2210
AD  - College of Mechanical and Electrical Engineering, Northeast Forestry University, 
      Harbin, 150040 China. GRID: grid.412246.7. ISNI: 0000 0004 1789 9091
FAU - Sun, Kangjian
AU  - Sun K
AD  - College of Mechanical and Electrical Engineering, Northeast Forestry University, 
      Harbin, 150040 China. GRID: grid.412246.7. ISNI: 0000 0004 1789 9091
LA  - eng
PT  - Journal Article
DEP - 20210513
PL  - England
TA  - Pattern Anal Appl
JT  - Pattern analysis and applications : PAA
JID - 101513778
PMC - PMC8116444
OTO - NOTNLM
OT  - Barnacles mating optimizer
OT  - Feature selection
OT  - Gaussian mutation
OT  - Logistic model
OT  - Refraction-learning
OT  - Support vector machine
COIS- Conflicts of interestThe authors declare that they have no known competing 
      financial interests or personal relationships that could have appeared to 
      influence the work reported in this paper.
EDAT- 2021/05/19 06:00
MHDA- 2021/05/19 06:01
PMCR- 2021/05/13
CRDT- 2021/05/18 07:08
PHST- 2020/07/28 00:00 [received]
PHST- 2021/04/29 00:00 [accepted]
PHST- 2021/05/19 06:00 [pubmed]
PHST- 2021/05/19 06:01 [medline]
PHST- 2021/05/18 07:08 [entrez]
PHST- 2021/05/13 00:00 [pmc-release]
AID - 985 [pii]
AID - 10.1007/s10044-021-00985-x [doi]
PST - ppublish
SO  - Pattern Anal Appl. 2021;24(3):1249-1274. doi: 10.1007/s10044-021-00985-x. Epub 
      2021 May 13.