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

محورهای موضوعی : Majlesi Journal of Telecommunication Devices

raheleh sharifi ¹ , Mohammadreza Ramezanpour ²

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

تاریخ دریافت : 1401/12/18 تاریخ پذیرش : 1402/02/24 تاریخ انتشار : 1402/09/10

کلید واژه: customer behavior, Classification, Prediction, Decision tree,

چکیده مقاله :

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.

چکیده انگلیسی:

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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