Heterogeneous Ensemble for Imaginary Scene Classification

Saleh Alyahyan, Majed Farrash, Wenjia Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)
14 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationProceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
Pages197-204
Number of pages8
DOIs
Publication statusPublished - 2016
Event8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Porto, Poland
Duration: 19 Sep 201623 Sep 2016

Conference

Conference8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
CountryPoland
CityPorto
Period19/09/1623/09/16

Keywords

  • Heterogenenous Ensemble
  • Big Data
  • Image Processing
  • Scene Classification
  • Diversity
  • Text mining

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