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Information Journal Paper

Title

Semi-Supervised Ensemble Using Confidence Based Selection Metric in Nnon-Stationary Data Streams

Pages

  197-208

Abstract

 In this article, we propose a novel Semi-Supervised Ensemble classifier using Confidence Based Selection metric, named SSE-CBS. The proposed approach uses labeled and unlabeled data, which aims at reacting to different types of concept drift. SSE-CBS combines an accuracy-based weighting mechanism known from block-based ensembles with the incremental nature of Hoeffding Tree. The proposed algorithm is experimentally compared to the state-of-the-art stream methods, including supervised, semi-supervised, single classifi ers, and block-based ensembles in different drift scenarios. Out of all the compared algorithms, SSE-CBS outperforms other semi-supervised ensemble approaches. Experimental results show that SSE-CBS can be considered suitable for scenarios, involving many types of drift in limited labeled data.

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    APA: Copy

    KHEZRI, SH., TANHA, J., AHMADI, A., & SHARIFI, A.. (2020). Semi-Supervised Ensemble Using Confidence Based Selection Metric in Nnon-Stationary Data Streams. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR, 18(3 ), 197-208. SID. https://sid.ir/paper/389039/en

    Vancouver: Copy

    KHEZRI SH., TANHA J., AHMADI A., SHARIFI A.. Semi-Supervised Ensemble Using Confidence Based Selection Metric in Nnon-Stationary Data Streams. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR[Internet]. 2020;18(3 ):197-208. Available from: https://sid.ir/paper/389039/en

    IEEE: Copy

    SH. KHEZRI, J. TANHA, A. AHMADI, and A. SHARIFI, “Semi-Supervised Ensemble Using Confidence Based Selection Metric in Nnon-Stationary Data Streams,” NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR, vol. 18, no. 3 , pp. 197–208, 2020, [Online]. Available: https://sid.ir/paper/389039/en

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