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MR-TP-QFPSO: map reduce two phases quantum fuzzy PSO for feature selection

Author

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  • Shikha Agarwal

    (Central University of South Bihar, CUSB)

  • Prabhat Ranjan

    (Central University of South Bihar, CUSB)

Abstract

Feature selection is the utmost requirement to deal with high dimensional datasets. Fuzzy logic and particle swarm optimization are the two very popular soft computing methods which have used for feature selection. In this paper different variants of PSO are summarized to explore the latest development in PSO. The survey has been grouped in three categories; structures based PSO variants, fuzzy logic-PSO hybrids and parallel PSO variants. On the basis of findings of survey, map reduce two phases quantum behaved fuzzy rule PSO (MR-TP-QFPSO) method has been proposed. Quantum is the smallest possible state of any matter. Therefore, in proposed method smallest state of any particle is trit, which is having three values 0, 1 and #. # is included to bring a state of uncertainty where, feature is considered neither accepted nor rejected. In first phase search, feature space is exhaustively explored. During exhaustive initial search (first phase), multiple subsets of features are selected using quantum behaved fuzzy rule PSO (QFPSO). From these multiple subsets, minimum most important features (lower bound features) and maximum range of selected features are selected (upper bound feature subset). In second phase, selected feature subspace (selected in first phase) has been exploited and finally merged with lower bound features. The entire two phases search is highly iterative and it is well known that map reduce frame work can accelerate any iterative task by parallel processing. Therefore, proposed two phases QFPSO (TP-QFPSO) is applied using map reduce (MR-TP-QFPSO). The analysis of proposed algorithm clearly shows that map reduce has decreased the processing time of serial TP-QFPSO algorithm. The MR-TP-QFPSO is compared with other feature selection methods. The results on bench marking datasets show that MR-TP-QFRPSO outperformed the other methods. The reduction in execution time is directly propositional to the number of cluster nodes used. Therefore, as number of nodes is increased execution time will decrease without affecting the performance.

Suggested Citation

  • Shikha Agarwal & Prabhat Ranjan, 2018. "MR-TP-QFPSO: map reduce two phases quantum fuzzy PSO for feature selection," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(4), pages 888-900, August.
    • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:4:d:10.1007_s13198-017-0682-9
    DOI: 10.1007/s13198-017-0682-9
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    References listed on IDEAS

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    1. Ibrahim Aljarah & Simone A. Ludwig, 2016. "A Scalable MapReduce-enabled Glowworm Swarm Optimization Approach for High Dimensional Multimodal Functions," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 7(1), pages 32-54, January.
    2. Jianfang Cao & Hongyan Cui & Hao Shi & Lijuan Jiao, 2016. "Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-17, June.
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    Cited by:

    1. Amitkumar Patil & Gunjan Soni & Anuj Prakash, 2022. "A BMFO-KNN based intelligent fault detection approach for reciprocating compressor," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 797-809, June.

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