A New Representation in PSO for Discretization-Based Feature Selection
In machine learning, discretization and feature selection (FS) are important techniques for preprocessing data to improve the performance of an algorithm on high-dimensional data. Since many FS methods require discrete data, a common practice is to apply discretization before FS. In addition, for the sake of efficiency, features are usually discretized individually (or univariate). This scheme works based on the assumption that each feature independently influences the task, which may not hold in cases where feature interactions exist. Therefore, univariate discretization may degrade the performance of the FS stage since information showing feature interactions may be lost during the discretization process. Initial results of our previous proposed method [evolve particle swarm optimization (EPSO)] showed that combining discretization and FS in a single stage using bare-bones particle swarm optimization (BBPSO) can lead to a better performance than applying them in two separate stages. In this paper, we propose a new method called potential particle swarm optimization (PPSO) which employs a new representation that can reduce the search space of the problem and a new fitness function to better evaluate candidate solutions to guide the search. The results on ten high-dimensional datasets show that PPSO select less than 5% of the number of features for all datasets. Compared with the two-stage approach which uses BBPSO for FS on the discretized data, PPSO achieves significantly higher accuracy on seven datasets. In addition, PPSO obtains better (or similar) classification performance than EPSO on eight datasets with a smaller number of selected features on six datasets. Furthermore, PPSO also outperforms the three compared (traditional) methods and performs similar to one method on most datasets in terms of both generalization ability and learning capacity. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Large-scale Evolutionary Feature Selection for Classification | Funder: ROYAL SOCIETY OF NEW ZEALAND | Grant ID: 16-VUW-111
Preferred citationTran, B., Xue, B. & Zhang, M. (2017). A New Representation in PSO for Discretization-Based Feature Selection. IEEE Transactions on Cybernetics, 48(6). https://doi.org/10.1109/TCYB.2017.2714145
Journal titleIEEE Transactions on Cybernetics
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
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Science & TechnologyTechnologyAutomation & Control SystemsComputer Science, Artificial IntelligenceComputer Science, CyberneticsComputer ScienceClassificationdiscretizationfeature selection (FS)high-dimensional dataparticle swarm optimization (PSO)PARTICLE SWARM OPTIMIZATIONCLASSIFICATIONALGORITHMArtificial Intelligence & Image ProcessingApplied MathematicsElectrical and Electronic Engineering