A new feature set partitioning method for nearest mean classifier ensembles

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 fea...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Ku-Mahamud, Ku Ruhana, Sediyono, Agung
التنسيق: Conference or Workshop Item
اللغة:English
منشور في: 2013
الموضوعات:
الوصول للمادة أونلاين:http://repo.uum.edu.my/11967/1/PID54.pdf
http://repo.uum.edu.my/11967/
http://www.icoci.cms.net.my/proceedings/2013/TOC.html
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الوصف
الملخص: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.