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Reinforcement learning for evolutionary distance metric learning systems improvement

Published: 06 July 2018 Publication History

Abstract

This paper introduces a hybrid system called R-EDML, combining the sequential decision making of Reinforcement Learning (RL) with the evolutionary feature prioritizing process of Evolutionary Distance Metric Learning (EDML) in clustering aiming to optimize the input space by reducing the number of selected features while maintaining the clustering performance. In the proposed method, features represented by the elements of EDML distance transformation matrices are prioritized. Then a selection control strategy using Reinforcement Learning is learned. R-EDML was compared to normal EDML and conventional feature selection. Results show a decrease in the number of features, while maintaining a similar accuracy level.

References

[1]
Ken-ichi Fukui, Satoshi Ono, Taishi Megano, and Masayuki Numao. 2013. Evolutionary distance metric learning approach to semi-supervised clustering with neighbor relations. In Tools with Artificial Intelligence (ICTAI), 2013 IEEE 25th International Conference on. IEEE, 398--403.
[2]
Janez Brest, Sao Greiner, Borko Boskovic, Marjan Mernik, and Viljem Zumer. 2006. Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE transactions on evolutionary computation 10, 6 (2006), 646--657.
[3]
Bassel Ali, Ken-ichi Fukui, Wasin Kalintha, Koichi Moriyama, and Masayuki Numao. 2017. Reinforcement learning based distance metric filtering approach in clustering. In Computational Intelligence (SSCI), 2017 IEEE Symposium Series on. IEEE, 1--8.
[4]
Girish Chandrashekar and Ferat Sahin. 2014. A survey on feature selection methods. Computers & Electrical Engineering 40, 1 (2014), 16--28.

Cited By

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  • (2020)Reinforcement learning based metric filtering for evolutionary distance metric learningIntelligent Data Analysis10.3233/IDA-19488724:6(1345-1364)Online publication date: 1-Jan-2020
  • (2020)Reinforcement Learning based Evolutionary Metric Filtering for High Dimensional Problems2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA51294.2020.00045(226-233)Online publication date: Dec-2020

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  1. Reinforcement learning for evolutionary distance metric learning systems improvement

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    cover image ACM Conferences
    GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion
    July 2018
    1968 pages
    ISBN:9781450357647
    DOI:10.1145/3205651
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 06 July 2018

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    Author Tags

    1. distance metric learning
    2. evolutionary algorithm
    3. reinforcement learning

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    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    View all
    • (2020)Reinforcement learning based metric filtering for evolutionary distance metric learningIntelligent Data Analysis10.3233/IDA-19488724:6(1345-1364)Online publication date: 1-Jan-2020
    • (2020)Reinforcement Learning based Evolutionary Metric Filtering for High Dimensional Problems2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA51294.2020.00045(226-233)Online publication date: Dec-2020

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