Heterogeneous Ensemble for Imaginary Scene Classification

Saleh Alyahyan, Majed Farrash, Wenjia Wang


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 and AdaBoost.


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Paper Citation

in Harvard Style

Alyahyan S., Farrash M. and Wang W. (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, (IC3K 2016) ISBN 978-989-758-203-5, pages 197-204. DOI: 10.5220/0006037101970204

in Bibtex Style

author={Saleh Alyahyan and Majed Farrash and Wenjia Wang},
title={Heterogeneous Ensemble for Imaginary Scene Classification},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)},

in EndNote Style

JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)
TI - Heterogeneous Ensemble for Imaginary Scene Classification
SN - 978-989-758-203-5
AU - Alyahyan S.
AU - Farrash M.
AU - Wang W.
PY - 2016
SP - 197
EP - 204
DO - 10.5220/0006037101970204