NDLI: A hybrid sampling method for imbalanced data

Content Provider	IEEE Xplore Digital Library
Author	Gazzah, S. Hechkel, A. Essoukri Ben Amara, N.
Copyright Year	2015
Description	Author affiliation: Nat. Eng. Sch. of Sousse, Univ. of Sousse, Sousse, Tunisia (Gazzah, S.; Hechkel, A.; Essoukri Ben Amara, N.)
Abstract	With the diversification of applications and the emergence of new trends in challenging applications such as in the computer vision domain, classical machine learning systems usually perform poorly while confronting two common problems: the training data of negative examples, which outnumber the positive ones, and the large intra-class variations. These problems lead to a drop in the system performances. In this work, we propose to improve the classification accuracy in the case of imbalanced training data by equally balancing a training data set using a hybrid approach which consists in over-sampling the minority class using a “SMOTE star topology”, and under-sampling the majority class by removing instances that are considered less relevant. The feature vector deletion has been performed with respect to intra-class variations, based on the distribution criterion. The experimental results, achieved in bio-metric data, show that the proposed approach significantly improves the overall performances measured in terms of true-positive rate.
Starting Page	1
Ending Page	6
File Size	420891
Page Count	6
File Format	PDF
ISBN	9781479917587
DOI	10.1109/SSD.2015.7348093
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-03-16
Publisher Place	Tunisia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Correlation Data analysis Databases Training data Imbalanced data sets One-against-all SVM Feature extraction Intra-class variations Principal component analysis
Content Type	Text
Resource Type	Article

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