In this paper a new classifier based on ant colony optimization in continuous domains (ACOR) has been proposed. In this classifier, ACOR is used to find the decision hyperplanes between the different classes. Also, a new cost function is proposed to find maximum-margin hyperplanes. The effectiveness of proposed classifier is evaluated by some common benchmarks with different feature space dimensions and number of classes. Also the performance of proposed classifier is compared with some of the conventional, heuristic and ACO-based classifier. The experimental results show that the performance of proposed classifier is comparable and sometimes better than compared classifiers.
DorigoM., Optimization, learning and natural algorithms, Ph D Thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy, (1992).
2.
DorigoM., ManiezzoV. and ColorniA., Ant system: Optimization by a colony of cooperating agents, IEEE Trans Systems, Man, CybernetPart B (1996), 29-41.
3.
DorigoM. and GambardellaL.M., Ant colony system: A cooperative learning approach to the traveling salesman problem, IEEE Trans on Evolutionary Computation (1997), 53-66.
4.
GagneC., PriceW.L. and GravelM., Comparing an ACO algorithm with other heuristics for the single machine scheduling problem with sequence-dependent setup times, The Journal of the Operational Research Society (2002), 895-906.
5.
BlumC. and SampelsM., An ant colony optimization algorithm for shop scheduling problems, Journal of Mathematical Modelling and Algorithms (2004), 285-308.
6.
SimK.M. and SunW.H., Ant colony optimization for routing and loadbalancing: Survey and new directions, IEEE Trans Syst, Man, Cybern A, Syst, Humans33 (2003) 560-572.
7.
IyengarS.S., WuH.C., BalakrishnanN. and ChangS.Y., Biologically inspired cooperative routing for wireless mobile sensor networks, IEEE Systems Journal, 1 (2007), 29-37.
8.
ZhangJ., ChungH.S.-H., LoA.W.-L. and HuangT., Extended ant colony optimization algorithm for power electronic circuit design, IEEE Trans Power Electron24 (2009), 147-162.
9.
SochaK. and DorigoM., Ant colony optimization for continuous domains, European Journal of Operational Research185 (2008), 1155-1173.
10.
MathurM., KaraleS.B., PriyeS., JayaramanV.K. and KulkarniB.D., Ant colony approach to continuous function optimization, Ind Eng Chem Res39 (2000), 3814-3822.
11.
MonmarcheN., VenturiniG. and SlimaneM., On how pachycondyla apicalis ants suggest a new search algorithm, Future Gener Comput Syst16 (2000), 937-946.
12.
DreoJ. and SiarryP., A new ant colony algorithm using the heterarchical concept aimed at optimization of multiminima continuous functions, in: Proceedings of the Third International Workshop on Ant Algorithms2463 (2002), 216-221.
13.
HuX.M., ZhangJ. and LiY., Orthogonal methods based ant colony search for solving continuous optimization problems, J Comput Sci Technol23 (2008), 2-18.
14.
OteroF.E.B., FreitasA.A. and JohnsonC.G., A new sequential covering strategy for inducing classification rules with ant colony algorithms, IEEE Transactions on Evolutionary Computation17 (2003), 64-76.
15.
AghdamM.H., Ghasem-AghaeeN. and BasiriM.E., Text feature selection using ant colony optimization, Expert Systems with Applications36 (2009), 6843-6853.
16.
KumarA., HanmandluM. and GuptaH.M., Ant colony optimization based fuzzy binary decision tree for bimodal hand knuckle verification system, Expert Systems with Applications40 (2013), 439-449.
17.
GanesanT., VasantP. and EamvazuthiI., Multiobjective optimization using particle swarm optimization with non-Gaussian random generators, Intelligent Decision Technologies10 (2016), 93-103.
18.
MiliosD., StamelosI. and ChatzibagiasC., A genetic algorithm approach to global optimization of software cost estimation by analogy, Intelligent Decision Technologies7 (2013), 45-58.
19.
StylianouC. and AndreouS., A multi-objective genetic algorithm for intelligent software project scheduling and team staffing, Intelligent Decision Technologies7 (2013), 59-80.
20.
VasantP. and BarsoumN., Hybrid genetic algorithms and line search method for industrial production planning with non-linear fitness function, Engineering Applications of Artificial Intelligence22 (2009), 767-777.
21.
VasantP., Meta-heuristics optimization algorithms in engineering, business, economics, and finance, IGI Global (2013), 1-734.
22.
VasantP., Handbook of research on artificial intelligence techniques and algorithms, IGI Global (2015), 1-796.
23.
VasantP., Handbook of research on novel soft computing intelligent algorithms: Theory and practical applications, IGI Global (2014).
24.
BandyopadhyayS., MurthyC.A. and PalS.A., Theoretical performance of genetic pattern classifier, Journal of the Franklin Institute (1999), 387-422.
25.
ZahiriS.H., Learning automata based classifier, Pattern Recognition Letters29 (2008), 40-48.
26.
ZahiriS.H. and SeyedinS.A., Swarm intelligence based classifiers, Journal of the Franklin Institute (2007), 362-376.
27.
ShahrakiH. and ZahiriS.-H., Classification of trapezoidal fuzzy data based on heuristic classifiers, Kasmera Journal (2015), 128-144.
28.
ShahrakiH. and ZahiriS.-H., Particle swarm classifier for fuzzy data sets, Paper Presented at the Artificial Intelligence and Signal Processing (2015).
29.
AskariH. and ZahiriS-H., Decision function estimation using intelligent gravitational search algorithm, International Journal of Machine Learning and Cybernetics3 (2012), 163-172.
30.
BahrololoumA., Nezamabadi-pourH., BahrololoumH. and SaeedM., A prototype classifier based on gravitational search algorithm, Applied Soft Computing12 (2012), 819-825.
31.
ZhengpingL., JiangtaoS., QiuzhenL., ZhihuaD., JianyongC. and ZhongM., A novel multiple rule sets data classification algorithm based on ant colony algorithm, Applied Soft Computing38 (2016), 1000-1011.
32.
OteroF., FreitasA. and JohnsonC., Inducing decision trees with an ant colony optimization algorithm, Applied Soft Computing12 (2012), 3615-3626.
