Xun Zhou
ABSTRACT
Feature selection is an important part of data preprocessing. There are currently three types of feature selection, namely filter, wrapper, and embedded feature selection methods. The latter is a combination of the former two. However, when the feature dimension is high and the amount of data is large, the filter method may not be able to extract a better subset, ignoring the interaction between the features, and the embedded algorithm has requirements for the learning model. Currently, wrapper algorithms face the problem of high computational overhead. The Spark platform based on in-memory computing has advantages in handling iterative tasks. Therefore, this paper proposes a chaotic parallel antlion optimization algorithm for feature selection (QPSALO) based on Spark, and conducts experimental comparisons on seven datasets. The results show that the QPSALO algorithm can improve the quality of candidate feature subsets while speeding up the execution.
- Zhen Cao and Clark Verbrugge. 2013. Mixed Model Universal Software Thread-Level Speculation. In 2013 42nd International Conference on Parallel Processing. 651--660. Google Scholar
Digital Library
- E. Emary and Hossam M. Zawbaa. 2019. Feature selection via Lèvy Antlion optimization. Pattern Analysis and Applications 22, 3 (2019), 857--876. Google ScholarDigital Library
- Fred Glover. 1986. Future paths for integer programming and links to artificial intelligence. Computers & Operations Research 13, 5 (1986), 533--549. Applications of Integer Programming. Google ScholarDigital Library
- Seyedali Mirjalili. 2015. The Ant Lion Optimizer. Advances in engineering software 83(1 2015), 80--98.Google Scholar
- Chandan Kumar Shiva and V. Mukherjee. 2015. A novel quasioppositional harmony search algorithm for automatic generation control of power system. Applied Soft Computing 35 (2015), 749--765. Google ScholarDigital Library
- Alaa Tharwat and Aboul Ella Hassanien. 2018. Chaotic antlion algorithm for parameter optimization of support vector machine. Appl. Intell. 48, 3 (2018), 670--686. Google ScholarDigital Library
- Wang Yadong, Shi Quan, Song Mingchang, and Song Weixing. 2019. Quasi-oppositional Multiobjective Antlion Optimizer Based on Differential Evolution. Journal of Physics: Conference Series 1267, 1 (jul 2019), 012010. Google ScholarCross Ref
- Dixiong Yang, Zhenjun Liu, and Ping Yi. 2017. Computational efficiency of accelerated particle swarm optimization combined with different chaotic maps for global optimization. Neural Computing and Applications 28, 1 (2017), 1245--1264. Google ScholarDigital Library
Index Terms
- A chaotic parallel antlion optimization algorithm for feature selection
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