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Prediction of users online purchase behavior based on selective ensemble learning

High Technology Letters 2023年第2期29卷 206-212页

作者：谭惠 DUAN Yong School of In formation Science Engineering Shenyang University of TechnologyShenyang 110870P.R.China

A probabilistic multi-dimensional selective ensemble learning method and its application in the prediction of users' online purchase behavior are studied in this ***, the classifier is integrated based on the dimension of predicted probability, and the pruning algorithm based on greedy forward search is obtained by combining the two indicators of accuracy and *** the pruning algorithm is integrated into the Stacking ensemble method to establish a user online shopping behavior prediction model based on the probabilistic multi-dimensional selective ensemble ***, the research method is compared with the prediction results of individual learners in ensemble learning and the Stacking ensemble method without *** experimental results show that the proposed method can reduce the scale of integration, improve the prediction accuracy of the model, and predict the user's online purchase behavior.

关键词： users'online purchase behavior Stacking selective ensemble ensemble pruning feature engineering

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A new correlation-based approach for ensemble selection in random forests

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International Journal of Intelligent Computing and Cybernetics 2021年第2期14卷 251-268页

作者： Mostafa El Habib Daho Nesma Settouti Mohammed El Amine Bechar Amina Boublenza Mohammed Amine Chikh Biomedical Engineering Laboratory University of TlemcenTlemcenAlgeria

Purpose-ensemble methods have been widely used in the field of pattern recognition due to the difficulty offinding a single classifier that performs well on a wide variety of *** the effectiveness of thesetechniques,studies have shown that ensemble methods generate a large number of hypotheses and thatcontain redundant classifiers in most *** works proposed in the state of the art attempt to reduce allhypotheses without affecting ***/methodology/approach-In this work,the authors are proposing a pruning method that takes intoconsideration the correlation between classifiers/classes and each classifier with the rest of the *** authorshave used the random forest algorithm as trees-based ensemble classifiers and the pruning was made by atechnique inspired by the CFS(correlation feature selection)***-The proposed method CES(correlation-based ensemble Selection)was evaluated onten datasets from the UCI machine learning repository,and the performances were compared to sixensemble pruning *** results showed that our proposed pruning method selects a smallensemble in a smaller amount of time while improving classification rates compared to the ***/value-CES is a new ordering-based method that uses the CFS *** selects,in a shorttime,a small sub-ensemble that outperforms results obtained from the whole forest and the other state-of-thearttechniques used in this study.

关键词： ensemble pruning Random forest Tree selection Correlation CFS CES

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