BIFFOA: A Novel Binary Improved Fruit Fly Algorithm for Feature Selection

被引:21
作者
Hou, Yun [1 ,3 ]
Li, Jingyao [1 ,2 ]
Yu, Haihong [1 ,2 ]
Li, Zhanshan [1 ,2 ]
机构
[1] Natl Educ Minist, Key Lab Symbol Computat & Knowledge Engn, Changchun 130012, Jilin, Peoples R China
[2] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Jilin, Peoples R China
[3] Jilin Univ, Coll Software, Changchun 130012, Jilin, Peoples R China
基金
中国国家自然科学基金;
关键词
Classification; evolutionary population dynamics; feature selection; fruit fly optimization algorithm; SALP SWARM ALGORITHM; OPTIMIZATION ALGORITHM;
D O I
10.1109/ACCESS.2019.2917502
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Feature selection is an important method to reduce the number of attributes of high-dimensional data and an essential preprocess work in classification. It eliminates irrelevant, redundant, and noisy features improves the performance of the model and reduces the computational burden. Fruit fly optimization algorithm is a newalgorithm proposed in recent years, which imitates the foraging behavior of fruit fly. To the best of our knowledge, it has not been systematically applied to feature selection. This paper uses the fruit fly optimization algorithm as a search strategy and designs a wrapper-based feature selection method, named binary improved fruit fly optimization algorithm (BIFFOA). Besides, four different strategies based on evolutionary population dynamics (EPD) and new mutation operators are employed to enhance the BIFFOA. The extensive experiments on 25 datasets (see Table 1) show that the performance of the BIFFOA is better than several state-of-the-art algorithms.
引用
收藏
页码:81177 / 81194
页数:18
相关论文
共 48 条
[1]  
[Anonymous], 2008, NATURE INSPIRED META
[2]  
Areej A., 2017, J INTELL LEARN SYST, V9, P422
[3]   Binary butterfly optimization approaches for feature selection [J].
Arora, Sankalap ;
Anand, Priyanka .
EXPERT SYSTEMS WITH APPLICATIONS, 2019, 116 :147-160
[4]   SELF-ORGANIZED CRITICALITY - AN EXPLANATION OF 1/F NOISE [J].
BAK, P ;
TANG, C ;
WIESENFELD, K .
PHYSICAL REVIEW LETTERS, 1987, 59 (04) :381-384
[5]  
COLORNI A, 1992, FROM ANIM ANIMAT, P134
[6]  
Crawford B., 2015, P INT C COMP SCI APP
[7]   Binary grey wolf optimization approaches for feature selection [J].
Emary, E. ;
Zawba, Hossam M. ;
Hassanien, Aboul Ella .
NEUROCOMPUTING, 2016, 172 :371-381
[8]   Grey wolf optimizer: a review of recent variants and applications [J].
Faris, Hossam ;
Aljarah, Ibrahim ;
Al-Betar, Mohammed Azmi ;
Mirjalili, Seyedali .
NEURAL COMPUTING & APPLICATIONS, 2018, 30 (02) :413-435
[9]   An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems [J].
Faris, Hossam ;
Mafarja, Majdi M. ;
Heidari, Ali Asghar ;
Aljarah, Ibrahim ;
Al-Zoubi, Ala' M. ;
Mirjalili, Seyedali ;
Fujita, Hamido .
KNOWLEDGE-BASED SYSTEMS, 2018, 154 :43-67
[10]   Research on collaborative negotiation for e-commerce. [J].
Feng, YQ ;
Lei, Y ;
Li, Y ;
Cao, RZ .
2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS, 2003, :2085-2088