As a combinatorial optimization problem, feature selection has been widely used in machine learning and data mining. In this paper, a feature selection method using forest optimization algorithm based on contribution degree is proposed. The proposed method uses a contribution degree strategy which is embedded in forest optimization algorithm. The goal of the contribution degree is to guide the search process of the forest optimization algorithm to select features according to high class correlation and low redundancy between features. The proposed algorithm is verified on some data sets from the UCI repository and the experiments show that the proposed method improves the classification accuracy compared with some other methods.
ChaghariA.Feizi-DerakhshiM.R. and BalafarM.A., Fuzzy clustering based on Forest optimization algorithm, Journal of King Saud University – Computer and Information Sciences30(1) (2016).
2.
FerreiraA.J. and FigueiredoM.A.T., An unsupervised approach to feature discretization and selection, Pattern Recognition45(9) (2012), 3048–3060.
3.
UnlerA.MuratA. and ChinnamR.B., mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification, Information Sciences181(20) (2011), 4625–4641.
4.
DadanehB.Z.MarkidH.Y. and ZakerolhosseiniA., Unsupervised probabilistic feature selection using ant colony optimization, Expert Systems with Applications53 (2016), 27–42.
5.
LaiC.ReindersM.J.T. and WesselsL., Random subspace method for multivariate feature selection, Pattern Recognition Letters27(10) (2006), 1067–1076.
6.
PascoalC.OliveiraM.R.PachecoA. et al., Theoretical evaluation of feature selection methods based on mutual information, Neurocomputing226 (2016), 168–181.
7.
PaulD.SuR.RomainM. et al., Feature selection for outcome prediction in oesophageal cancer using genetic algorithm and random forest classifier, Compute Med Imaging Graph60 (2016), 42.
8.
TrucDV., Enhancing forest optimization algorithm for order acceptance and scheduling, Proceedings of the Asia Pacific Industrial Engineering & Management Systems Conference, 2015.
9.
TrucDV., Using discrete forest optimization algorithm for independent jobs scheduling on computational grids with local search, Proceedings of the Asia Pacific Industrial Engineering & Management Systems Conference, 2015.
10.
HancerE.XueB. and KarabogaD. et al., A binary ABC algorithm based on advanced similarity scheme for feature selection, Applied Soft Computing36(0)(2015), 334–348.
11.
ZorarpacıE. and ÖzelS.A., A hybrid approach of differential evolution and artificial bee colony for feature selection, Pergamon Press, Inc. 2016.
12.
LiF.MiaoD. and PedryczW., Granular multi-label feature selection based on mutual information, Elsevier Science Inc, 2017.
13.
ChandrashekarG. and SahinF., A survey on feature selection methods, Pergamon Press, Inc. 2014.
14.
PengH.LongF. and DingC., Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy, IEEE Computer Society, 2005.
15.
WangH. and NiuB., A novel bacterial algorithm with randomness control for feature selection in classification, Neurocomputing (2017), 228.
16.
GheyasI.A. and SmithL.S., Feature subset selection in large dimensionality domains, Pattern Recognition43(1) (2010), 5–13.
17.
BiesiadaJ. and DuchW., Feature selection for high-dimensional data – a pearson redundancy based filter, Advances in Soft Computing45 (2007), 242–249.
18.
CheJ.YangY.LiL. et al., Maximum relevance minimum common redundancy feature selection for nonlinear data, Information Sciences (2017), 409.
19.
HuangJ. and RongP., A hybrid genetic algorithm for feature selection based on mutual information, Pattern Recognition Letters28(13) (2007), 1825–1844.
20.
RaileanuL.E. and StoffelK., Theoretical comparison between the gini index and information gain criteria, Annals of Mathematics & Artificial Intelligence41(1) (2004), 77–93.
21.
YuL. and LiuH., Feature selection for high-dimensional data: A fast correlation-based filter solution, Twentieth International Conference on International Conference on Machine Learning, AAAI Press, 2003, pp. 856–863.
22.
TahirM.A.BouridaneA. and KurugolluF., Simultaneous feature selection and feature weighting using Hybrid Tabu Search/-nearest neighbor classifier, Pattern Recognition Letters28(4) (2007), 438–446.
23.
BennasarM.HicksY. and SetchiR., Feature selection using Joint Mutual Information Maximisation, Expert Systems with Applications42(22) (2015), 8520–8532.
24.
ElalamiM.E., A filter model for feature subset selection based on genetic algorithm, Knowledge-Based Systems22(5) (2009), 356–362.
25.
GhaemiM. and Feizi-DerakhshiM.R., Feature selection using forest optimization algorithm, Pattern Recognition60 (2016), 121–129.
26.
GhaemiM. and Feizi-DerakhshiM.R., Forest Optimization Algorithm, Expert Systems with Applications41(15) (2014), 6676–6687.
27.
HaindlM.SomolP.VerveridisD. et al., Feature selection based on mutual correlation, Lecture Notes in Computer Science, 2006.
28.
KabirM.M.ShahjahanM. and MuraseK., A new local search based hybrid genetic algorithm for feature selection, Neurocomputing74(17) (2011), 2914–2928.
29.
PalM. and FoodyG.M., Feature selection for classification of hyperspectral data by SVM, IEEE Transactions on Geoscience & Remote Sensing48(5) (2010), 2297–2307.
30.
FriedmanN.GeigerD. and GoldszmidtM., Bayesian network classifiers, Machine Learning29(2–3) (1997), 131–163.
31.
HoqueN.BhattacharyyaD.K. and KalitaJ.K., MIFS-ND: A mutual information-based feature selection method,Expert Systems with Applications41(14)(2014), 6371–6385.
32.
VinhN.X.ZhouS.ChanJ. et al., Can high-order dependencies improve mutual information based feature selection?Pattern Recognition53 (2016), 46–58.
33.
CastilloO.SoriaJ.MelinP. et al., New approach using ant colony optimization with ant set partition for fuzzy control design applied to the ball and beam system,Information Sciences294 (2015),203–215.
34.
MoradiP. and RostamiM.,Integration of graph clustering with ant colony optimization for feature selection, Knowledge-Based Systems84 (2015), 144–161.
35.
ShunmugapriyaP. andKanmaniS.,A hybrid algorithm using ant and bee colony optimization for feature selection and classification (AC-ABC Hybrid), Swarm & Evolutionary Computation, 2017, 36.
36.
GuQ.LiZ. and HanJ., Generalized fisher score for feature selection, Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, AUAI Press, 2011, pp. 266–273.
37.
YangQ.WangM.XiaoH. et al., Feature selection using a combination of genetic algorithm and selection frequency curve analysis, Chemometrics & Intelligent Laboratory Systems148 (2015), 106–114.
38.
SheikhpourR.SarramM.A.GharaghaniS. et al., A survey on semi-supervised feature selection methods, Pattern Recognition64(2) (2016), 141–158.
39.
MahmoudiS. and LailypourC., A discrete binary version of the forest optimization algorithm, International Conference on Information Technology, Computer & Communication, 2015.
40.
MoustakidisS.P. and TheocharisJ.B., SVM-FuzCoC: A novel SVM-based feature selection method using a fuzzy complementary criterion, Pattern Recognition43(11) (2010), 3712–3729.
41.
TabakhiS.MoradiP. and AkhlaghianF., An unsupervised feature selection algorithm based on ant colony optimization, Engineering Applications of Artificial Intelligence32(6) (2014), 112–123.
MaT.WangY.TangM. et al., LED: A fast overlapping communities detection algorithm based on structural clustering, Neurocomputing207 (2016), 488–500.
44.
MaT.ZhangY.CaoJ. et al., KDVEM: A k-degree anonymity with vertex and edge modification algorithm, Computing97(12) (2015), 1165–1184.
45.
SugumaranV.MuralidharanV. and RamachandranK.I., Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing, Mechanical Systems & Signal Processing21(2) (2007), 930–942.
46.
HeX.CaiD. and NiyogiP., Laplacian score for feature selection, International Conference on Neural Information Processing Systems, MIT Press, 2005, pp. 507–514.
47.
LiuX. and ShangL., A fast wrapper feature subset selection method based on binary particle swarm optimization, Evolutionary Computation, IEEE, 2013,, pp. 3347–3353.
48.
FarziY. and RezazadehH., A new forest optimization algorithm for solving multidimensional knapsack problem, The International Conference in New Research of Industry & Mechanical, 2015.
49.
LvY.MaT.TangM. et al., An efficient and scalable density-based clustering algorithm for datasets with complex structures, Neurocomputing171 (2016), 9–22.
50.
SaeysY.InzaI. and LarrañagaP., WLD: Review of feature selection techniques in bioinformatics, Bioinformatics23(19) (2007), 2507–2517.
51.
XueY.JiangJ.ZhaoB. et al., A self-adaptive artificial bee colony algorithm based on global best for global optimization, Soft Computing8 (2017).
52.
ZhangY.GongD.HuY. et al., Feature selection algorithm based on bare bones particle swarm optimization, Neurocomputing148(1) (2015), 150–157.
53.
BeheshtiZ. and ShamsuddinS.M., A review of population-based meta-heuristic algorithm, International Journal of Advances in Soft Computing & Its Applic5(1) (2013), 1–35.
