A novel clustering-based ensemble classification model for block learning

Mohammad Raihanul Islam, Mustafizur Rahman, Asif Salekin, Shihab Hasan Chowdhury, Samiul Alam Anik

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

Abstract

In this paper, we have considered a real life scenario where data is available in blocks over the period of time. We have developed a dynamic cluster based ensemble of classifiers for the problem. We have applied clustering algorithm on the block of data available at that time and have trained a neural network for each of the clusters. The performance of the network is tested against the next available block of data and based on that performance the parameters of the clustering algorithm is changed at runtime. In our approach increasing the number of clusters is considered as changing of the parameter settings. The misclassified instances of the test data are also joined with the training data to refine the knowledge of the classifiers. The proposed system is capable of identifying the decision boundary of different classes based on the current block of data more precisely. An extensive experiments has been performed to evaluate this dynamic system and to compute the optimal parameters of the proposed procedure.

Original languageEnglish (US)
Title of host publicationICPRAM 2013 - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods
Pages285-288
Number of pages4
StatePublished - May 27 2013
Externally publishedYes
Event2nd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2013 - Barcelona, Spain
Duration: Feb 15 2013Feb 18 2013

Publication series

NameICPRAM 2013 - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods

Conference

Conference2nd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2013
CountrySpain
CityBarcelona
Period2/15/132/18/13

Keywords

  • Artificial neural network
  • Ensemble classifier
  • K-means clustering

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Fingerprint Dive into the research topics of 'A novel clustering-based ensemble classification model for block learning'. Together they form a unique fingerprint.

  • Cite this

    Islam, M. R., Rahman, M., Salekin, A., Chowdhury, S. H., & Anik, S. A. (2013). A novel clustering-based ensemble classification model for block learning. In ICPRAM 2013 - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods (pp. 285-288). (ICPRAM 2013 - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods).