Bassel Ali
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.
- 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. Google Scholar
Digital Library
- 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. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- Girish Chandrashekar and Ferat Sahin. 2014. A survey on feature selection methods. Computers & Electrical Engineering 40, 1 (2014), 16--28. Google ScholarDigital Library
Index Terms
- Reinforcement learning for evolutionary distance metric learning systems improvement
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