Detecting the First Moment Of Truth in Online Shopping Using Data Preprocessing Methods and Ensemble Classifiers

Articles in Press

Document Type : Original Article

Authors

Department of Computer Engineering, Faculty of Engineering, Arak University, Arak, Iran

10.22052/scj.2024.253114.1156

Abstract

In this article, we present a strategy with the aim of increasing the accuracy of early detection of buyers from customers browsing in an online store. Nowadays, people tend to explore online to find the items they need and buy through online transactions. However, the number of actual buyers is still very low compared to the total number of visitors to these sites. Behavioral analysis, prediction and early identification of visitors who intend to buy from the online store provides the basis for providing more suitable customized content for them. From a managerial point of view, this time is called the First Moment of Truth (FMoT). The main advantage of this precedent is reducing the risk of losing users with high purchase probability and increasing the conversion rate. Due to the consistency of the prediction and diagnosis framework in data mining, the focus of this article is on the optimal use of pre-processing methods, with the aim of improving the quality of input data to classification algorithms. For this reason, in the proposed strategy, we use a set of algorithms for converting nominal content into numerical, normalization, outlier data detection, feature selection and balancing. Then, we give the modified data to a set of different classification algorithms, including C4.5 decision tree and multi-layer perceptron, and combined classification algorithms of random forest, bagging and gradient boosting. The evaluation of the results shows that the highest amount of accuracy obtained in this research by using ensemble classifiers has reached 94.42%,

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Soft Computing Journal

Articles in Press, Accepted Manuscript
Available Online from 21 November 2023

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History

  • Receive Date: 15 June 2023
  • Revise Date: 10 October 2023
  • Accept Date: 21 November 2023

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