Sainin, Mohd Shamrie and Ahmad, Faudziah and Alfred, Rayner (2015) Ensemble classifier and resampling for imbalanced multiclass learning. In: 5th International Conference on Computing and Informatics (ICOCI) 2015, 11-13 August 2015, Istanbul, Turkey.

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Abstract

An ensemble classifier called DECIML has previously reported that the classifier is able to perform on benchmark data compared to several single classifiers and ensemble classifiers such as AdaBoost, Bagging and Random Forest.The implementation of the ensemble using sampling was carried out in order to investigate if there are any improvements in the classification performances of the DECIML.Random sampling with replacement (SWR) method is applied to minority class in the imbalanced multiclass data. Results show that the SWR is able to increase the average performance of the ensemble classifier

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	ISBN No: 978-967-0910-02-4 Jointly organized by: Universiti Utara Malaysia & Istanbul Zaim University
Uncontrolled Keywords:	ensemble classifier, DECIML, imbalance, multiclass, data mining, machine learning, sampling
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	School of Computing
Depositing User:	Mr. Mohd. Shamrie Sainin
Date Deposited:	12 Oct 2015 08:08
Last Modified:	27 Apr 2016 08:46
URI:	https://repo.uum.edu.my/id/eprint/15678

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