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

Majlesi Journal of Telecommunication Devices

دوره 12، شماره 4 - شماره پیاپی 48، اسفند 2023، صفحه 177-188   

نوع مقاله: Reseach Article

شناسه دیجیتال (DOI): 10.30486/mjtd.2023.1980072.1026

نویسندگان

raheleh sharifi¹؛ Mohammadreza Ramezanpour^{* 2}

¹Department of Computer Engineering, Majlesi Branch, Islamic Azad University, Isfahan, Iran.

²Department of Computer Engineering, Mobarakeh Branch, Islamic Azad University, Isfahan, Iran

چکیده

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.

کلیدواژه‌ها

Customer behavior؛ Decision tree؛ Classification؛ Prediction

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