An Improved Decision Tree Classification Method based on Wild Horse Optimization Algorithm

Subject Areas : Majlesi Journal of Telecommunication Devices

raheleh  sharifi ¹ (Department of Computer Engineering, Majlesi Branch, Islamic Azad University, Isfahan, Iran.)
Mohammadreza  Ramezanpour ² (Department of Computer Engineering, Mobarakeh Branch, Islamic Azad University, Isfahan, Iran)

Received: 2023-03-09 Accepted : 2023-05-14 Published : 2023-12-01

Keywords: customer behavior, Classification, Prediction, Decision tree,

Abstract :

In this paper, an improved decision tree classification method based on wild horse optimization algorithm is proposed and then the application in customer behavior analysis is evaluated. Customer behavior is modeled in the form of time series. The proposed method includes two general steps. First, the customers are classified into clusters based on the features extracted from the time series, and then the customers’ behavior is estimated based on an efficient predictive algorithm in the second step. In this paper, an improved decision tree classification based on wild horse optimization algorithm is used to predict customer behavior. The proposed method is implemented in the MATLAB software environment and its efficiency is evaluated in the Symmetric Mean Absolute Percentage Error (SMAPE) index. The experimental results show that variance, spikiness, lumpiness and entropy have a high impact intensity among the extracted features. The overall evaluation indicate that this proposed method obtains the lowest prediction error in compared to other evaluated methods.

References:

• T. Hien, Y.-L. Su, R. Sann, and L. T. P. Thanh, "Analysis of Online Customer Complaint Behavior in Vietnam’s Hotel Industry," Sustainability, vol. 14, no. 7, p. 3770, 2022.


• Dogan, O. F. Seymen, and A. Hiziroglu, "Customer Behavior Analysis by Intuitionistic Fuzzy Segmentation: Comparison of Two Major Cities in Turkey," International Journal of Information Technology & Decision Making, vol. 21, no. 02, pp. 707-727, 2022.


• T. Tang, H.-H. Wu, H.-W. Yang, J. I. Shieh, and M. M. Lo, "Customer Behavior Analysis of the Chinese New Year from a Supermarket in Taiwan," in 2022 2nd International Conference on Information Technology and Education (ICIT&E), 2022, pp. 156-159: IEEE.


• Nazer, and R. Khorsand, "Energy Aware Resource Provisioning for Multi-Criteria Scheduling in Cloud Computing," Cybernetics and Systems, 2022 Apr 30:1-30.


• M. Baashar, A. K. Mahomood, M. A. Almomani, and G. A. Alkawsi, "Customer relationship management (CRM) in healthcare organization: A review of ten years of research," in Computer and Information Sciences (ICCOINS), 2016 3rd International Conference on, 2016, pp. 97-102: IEEE.


• Abbasimehr and M. Shabani, "A new methodology for customer behavior analysis using time series clustering: A case study on a bank’s customers," Kybernetes, 2019.


• C. Gopal and L. Jacob, "Customer Behavior Analysis Using Unsupervised Clustering and Profiling: A Machine Learning Approach," in 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2022, pp. 2075-2078: IEEE.


• Tavousi, S. Azizi, and A. Ghaderzadeh, "A fuzzy approach for optimal placement of IoT applications in fog-cloud computing," Cluster Computing, vol. 25, no. 1, pp. 303-320, 2022.


• Breiman, Classification and regression trees. Routledge, 2017.


• Saberi, M. Ramezanpour, and R. Khorsand, "An efficient data hiding method using the intra prediction modes in HEVC," Multimedia Tools and Applications 79 (2020): 33279-33302.


• B. Kotsiantis, "Decision trees: a recent overview," Artificial Intelligence Review, vol. 39, no. 4, pp. 261-283, 2013.


• Tharwat and A. E. Hassanien, "Chaotic antlion algorithm for parameter optimization of support vector machine," Applied Intelligence, vol. 48, no. 3, pp. 670-686, 2018.


• R Sharifi, M Ramezanpour, ”Customer Behavior Analysis using Wild Horse Optimization Algorithm,” Majlesi Journal of Telecommunication Devices, 2023.


• Abbasimehr and F. S. Baghery, "A novel time series clustering method with fine-tuned support vector regression for customer behavior analysis," Expert Systems with Applications, p. 117584, 2022.


• Bandara, C. Bergmeir, and S. Smyl, "Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach," Expert systems with applications, vol. 140, p. 112896, 2020.


• Norouzi, A. Arshaghi, and M. Ashourian, "Encryption of Color Images using Pixel Shift Algorithm and Developed Hill Algorithm,"Majlesi Journal of Telecommunocation Devices, vol. 11, no. 4, pp.177-184, 2022.


• Hosseini, N. Abdolvand, and S. R. Harandi, "Two-dimensional analysis of customer behavior in traditional and electronic banking," Digital Business, vol. 2, no. 2, p. 100030, 2022.


• Hosseini and M. Shabani, "New approach to customer segmentation based on changes in customer value," Journal of Marketing Analytics, vol. 3, no. 3, pp. 110-121, 2015.


• Ma, Z. Li, and D. Zheng, "Analysis of Chinese consumers’ willingness and behavioral change to purchase Green agri-food product online," Plos one, vol. 17, no. 4, p. e0265887, 2022.


• Baratzadeh and S. M. Hasheminejad, "Customer Behavior Analysis to Improve Detection of Fraudulent‎ Transactions using Deep Learning," Journal of AI and Data Mining, vol. 10, no. 1, pp. 87-101, 2022.


• He, D. Cai, and P. Niyogi, "Laplacian score for feature selection," Advances in neural information processing systems, vol. 18, pp. 507-514, 2005.


• Kowsari, K. Jafari Meimandi, M. Heidarysafa, S. Mendu, L. Barnes, and D. Brown, "Text classification algorithms: A survey," Information, vol. 10, no. 4, p. 150, 2019.


• Nayak, B. Naik, and H. Behera, "A comprehensive survey on support vector machine in data mining tasks: applications & challenges," International Journal of Database Theory and Application, vol. 8, no. 1, pp. 169-186, 2015.


• Zhang and Y. Guo, "Optimization of SVM parameters based on PSO algorithm," in 2009 Fifth International Conference on Natural Computation, 2009, vol. 1, pp. 536-539: IEEE.


• Mafarja, I. Aljarah, H. Faris, A. I. Hammouri, A.-Z. Ala’M, and S. Mirjalili, "Binary grasshopper optimisation algorithm approaches for feature selection problems," Expert Systems with Applications, vol. 117, pp. 267-286, 2019.


• Martínez, M. P. Frías, M. D. Pérez-Godoy, and A. J. Rivera, "Dealing with seasonality by narrowing the training set in time series forecasting with kNN," Expert systems with applications, vol. 103, pp. 38-48, 2018.