Hybrid clonal selection algorithm with Hopfield neural network for 3-satisfiability data mining on Amazon's Employees Resources Access

Abstract

Clonal Selection Algorithm (CSA) is a sturdy evolutionary algorithm that mimics the biological immune system mechanism, frequently implemented as a searching technique in solving complex modelling tasks such as pattern recognition and combinatorial optimization. Subsequently, 3-Satisfiability logic mining is the fundamental medium to induce real data sets in a more precise manner. In this paper, a modified clonal selection algorithm with Hopfield neural network (HNN) is proposed for 3-Satisfiability data mining. Somatic hypermutation operator in CSA can reduce the iterations during HNN training phase and retrieve the best logical rule for a minimal testing error. In addition, 3- Satisfiability logical rule provides better attributes representation of the data set. The proposed method will be applied on Amazon's Employees Resources Access data set to predict the approval or denial for an unseen set of employees in the future. The performance evaluation of the proposed method will be compared with exhaustive search (ES) by computing plausible errors such as root mean square error (RMSE), mean absolute error (MAE), sum of squared error (SSE), accuracy and computational time. The computational simulations are carried out by manipulating different number of neurons to verify the capability of the proposed method in data mining. The experimental results have shown a better performance of the proposed model in training and retrieval phase of the Amazon's Employees Resources Access data set in comparison with ES used in this research.