MD5 Encryption Algorithm Enhanced Competitive Swarm Optimizer for Feature Selection

Feature selection plays a crucial role in machine learning. For evolutionary computation based feature selection, the same particles may be repeatedly generated many times during the population iteration process, and recalculating the fitness values of these particles will cost a large amount of computational resources. Therefore, it is necessary to find these particles fast and skip calculating their fitness to save computation resources. This paper proposes the combination of MD5 encryption algorithm and the competitive swarm optimizer (CSO) algorithm for feature selection. Every particle in the population generated by CSO is encoded with MD5 encryption algorithm. Then the HashMap is used to quickly search for the repetitive particles. Through MD5 coding and HashMap searching, the repetitive particles are found fast, and then the recalculation of the repeated particles are avoid. Experimental results show that our proposed algorithm can significantly reduce the running time of feature selection for low-dimensional data, medium-dimensional data and high-dimensional data. Moreover, our algorithm does not change the framework of the competitive population optimization algorithm, only avoids calculation of the repeatedly generated particles, and therefore has no effect on the accuracy and number of features. Our algorithm can be widely applied to other computational intelligence methods.

[1]  Harpreet Singh,et al.  A New Hybrid Algorithm Based on Grey Wolf Optimization and Crow Search Algorithm for Unconstrained Function Optimization and Feature Selection , 2019, IEEE Access.

[2]  Message Digest (MD5) Algorithm and Secure Hash Algorithm (SHA) , 2008, Encyclopedia of Multimedia.

[3]  Jun Zhang,et al.  A dynamic competitive swarm optimizer based-on entropy for large scale optimization , 2016, 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI).

[4]  Wei-jie Yu,et al.  Competitive Swarm Optimizer with Dynamic Grouping for Large Scale Optimization , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

[5]  Ian H. Witten,et al.  Data Mining: Practical Machine Learning Tools and Techniques, 3/E , 2014 .

[6]  Yaochu Jin,et al.  A Competitive Swarm Optimizer for Large Scale Optimization , 2015, IEEE Transactions on Cybernetics.

[7]  Matti Lehtonen,et al.  Machine Learning Based Integrated Feature Selection Approach for Improved Electricity Demand Forecasting in Decentralized Energy Systems , 2019, IEEE Access.

[8]  Jianchao Zeng,et al.  Fitness Estimation Strategy Assisted Competitive Swarm Optimizer for High Dimensional Expensive Problems , 2016, GECCO.

[9]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[10]  J T Tarigan,et al.  A comparative study of Message Digest 5(MD5) and SHA256 algorithm , 2018 .

[11]  Narayanan Kumarappan,et al.  Optimal installation of multiple DG units using competitive swarm optimizer (CSO) algorithm , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[12]  Yaochu Jin,et al.  Feature selection for high-dimensional classification using a competitive swarm optimizer , 2016, Soft Computing.

[13]  Haihong Yu,et al.  BIFFOA: A Novel Binary Improved Fruit Fly Algorithm for Feature Selection , 2019, IEEE Access.

[14]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[15]  Shuqiang Huang,et al.  Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems , 2017, Sensors.

[16]  Kedar Nath Das,et al.  A modified competitive swarm optimizer for large scale optimization problems , 2017, Appl. Soft Comput..

[17]  Ronald L. Rivest,et al.  The MD5 Message-Digest Algorithm , 1992, RFC.

[18]  Dongyuan Shi,et al.  Orthogonal learning competitive swarm optimizer for economic dispatch problems , 2018, Appl. Soft Comput..

[19]  Said Jadid Abdul Kadir,et al.  Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection , 2019, IEEE Access.