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Hybridization of Adaboost with Random Forest for Real-Time Prediction of Online Shoppers’ Purchasing Intention

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Hybrid Intelligent Systems (HIS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1375))

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Abstract

Recently, a real-time online shopper behavior prediction system based on random forest has been suggested to predict the visitor’s shopping intent as soon as the website is visited. The main focus of the current paper is to consolidate the previously suggested system by using hybridization of adaboost with random forest. As in the former study, the proposed system relies on session and visitor information and uses oversampling to improve the performance and the scalability of classification. The results show that the novel system based on adaboost and random forest ensemble classification outstrips the former one in terms of accuracy and F1 score.

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Authors and Affiliations

  1. Teolia Consulting, 12–14 Rond-Point des Champs Elysées, 75008, Paris, France

    Karim Baati

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  1. Karim Baati
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Correspondence to Karim Baati .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, Olten, Solothurn, Switzerland

    Thomas Hanne

  3. Division of Graduate Studies, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, AL, USA

    Niketa Gandhi

  5. Universidade Federal da Bahia, Salvador, Bahia, Brazil

    Tatiane Nogueira Rios

  6. Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

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Baati, K. (2021). Hybridization of Adaboost with Random Forest for Real-Time Prediction of Online Shoppers’ Purchasing Intention. In: Abraham, A., Hanne, T., Castillo, O., Gandhi, N., Nogueira Rios, T., Hong, TP. (eds) Hybrid Intelligent Systems. HIS 2020. Advances in Intelligent Systems and Computing, vol 1375. Springer, Cham. https://doi.org/10.1007/978-3-030-73050-5_23

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