Heterogeneous Ensemble for Imaginary Scene Classification

Alyahyan, Saleh, Farrash, Majed and Wang, Wenjia (2016) Heterogeneous Ensemble for Imaginary Scene Classification. In: Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR. UNSPECIFIED, pp. 197-204. ISBN 978-989-758-203-5

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    Abstract

    In data mining, identifying the best individual technique to achieve very reliable and accurate classification has always been considered as an important but non-trivial task. This paper presents a novel approach - heterogeneous ensemble technique, to avoid the task and also to increase the accuracy of classification. It combines the models that are generated by using methodologically different learning algorithms and selected with different rules of utilizing both accuracy of individual modules and also diversity among the models. The key strategy is to select the most accurate model among all the generated models as the core model, and then select a number of models that are more diverse from the most accurate model to build the heterogeneous ensemble. The framework of the proposed approach has been implemented and tested on a real-world data to classify imaginary scenes. The results show our approach outperforms other the state of the art methods, including Bayesian network, SVM an d AdaBoost.

    Item Type: Book Section
    Uncontrolled Keywords: heterogenenous ensemble,big data,image processing,scene classification,diversity,text mining
    Faculty \ School: Faculty of Science > School of Computing Sciences
    University of East Anglia > Faculty of Science > Research Groups > Computational Biology (subgroups are shown below) > Machine learning in computational biology
    Depositing User: Pure Connector
    Date Deposited: 21 Dec 2016 00:04
    Last Modified: 10 Oct 2017 01:44
    URI: https://ueaeprints.uea.ac.uk/id/eprint/61839
    DOI: 10.5220/0006037101970204

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