过采样
计算机科学
决策树
机器学习
人工智能
朴素贝叶斯分类器
分类器(UML)
投票
集成学习
随机森林
Boosting(机器学习)
预测建模
逻辑回归
数据挖掘
支持向量机
带宽(计算)
法学
政治
计算机网络
政治学
作者
Anshika Arora,Sakshi Sakshi,Umesh Gupta
出处
期刊:Lecture notes in networks and systems
日期:2023-10-25
卷期号:: 741-752
标识
DOI:10.1007/978-981-99-4071-4_57
摘要
As customer traffic has been increasing over the years on online shopping websites, it is indispensable for sellers to assess online customers’ purchase intentions, which can potentially be predicted by analyzing the historical activities of the customers. This study analyzes the highly imbalanced empirical data of online shoppers’ intentions to foretell whether a visitor to an online shopping website will make a purchase. The synthetic minority oversampling technique has been implemented to reconstruct the dataset to alleviate the class imbalance in the original dataset. The effectiveness of oversampling has been identified by comparing the predictive performance of four different classifiers Partial decision tree (PART), decision tree (DT), Naïve Bayes (NB), and logistic regression (LR) on the reconstructed data with the performance on the original dataset. It has been observed that each classifier performs better on the reconstructed dataset. Ensemble learners have been implemented with varying base classifiers on the reconstructed dataset to identify the best predictive model. Bagging, boosting, and max-voting ensemble learners have been implemented with the base classifiers PART, DT, NB, and LR. The best performance has been observed by the prediction using bagging with PART as the base classifier with an accuracy of 92.62%. Hence, it has been identified as the best model for predicting the purchase intention of a customer in terms of accuracy. However, the highest precision and recall values of 0.923 have been given by the max-voting classifier with DT, PART, and LR as the base learners. It has also been concluded that the proposed methodology outperforms the existing models for shoppers’ intention section tasks.
科研通智能强力驱动
Strongly Powered by AbleSci AI