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

الموضوعات : 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)

تاريخ الإرسال : 17 الخميس , شعبان, 1444 تاريخ التأكيد : 24 الأحد , شوال, 1444 تاريخ الإصدار : 17 الجمعة , جمادى الأولى, 1445

الکلمات المفتاحية: customer behavior, Classification, Prediction, Decision tree,

ملخص المقالة :

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.

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