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A new feature set partitioning method for nearest mean classifier ensembles


Ku-Mahamud, Ku Ruhana and Sediyono, Agung (2013) A new feature set partitioning method for nearest mean classifier ensembles. In: 4th International Conference on Computing and Informatics (ICOCI 2013), 28 -30 August 2013, Kuching, Sarawak, Malaysia.

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Abstract

Nearest Mean Classifier (NMC)provides good performance for small sample size problem. However concatenate different features into a high dimensional feature vectors and process them using a single NMC generally does not give good results because of dimensionality problem.In this new method, the feature set is partitioned into disjoint feature subset based on diversity in ensemble.NMC ensemble is constructed by assigning each individual classifier in the ensemble with a cluster from different feature subset.The advantage of this method is that all available information in the training set is used.There is no irrelevant feature in the training set that was eliminated.Based on experimental results the new method shows a significant improvement with high statistical confidence.

Item Type: Conference or Workshop Item (Paper)
Additional Information: ISBN: 9789832078791 Organized by: Universiti Utara Malaysia
Uncontrolled Keywords: nearest mean classifier, feature set partitioning, ensemble classifier
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: College of Arts and Sciences
Depositing User: Prof. Dr. Ku Ruhana Ku Mahamud
Date Deposited: 24 Aug 2014 01:42
Last Modified: 08 Apr 2015 02:06
URI: http://repo.uum.edu.my/id/eprint/11967

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