NDLI: Training set size reduction in large dataset problems

Content Provider	IEEE Xplore Digital Library
Author	Chouvatut, Varin Jindaluang, Wattana Boonchieng, Ekkarat
Copyright Year	2015
Description	Author affiliation: The Theoretical and Empirical Research Group, Center of Excellence in Community Health Informatics, Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand (Chouvatut, Varin; Jindaluang, Wattana; Boonchieng, Ekkarat)
Abstract	Classifiers have known to be used in various fields of applications. However, the main problem usually found recently is about applying a classifier to large datasets. Thus, the process of reducing size of the training set becomes necessary especially to accelerate the processing time of the classifier. Concerning the problem, this paper proposes a new method which can reduce size of the training set in a large dataset. Our proposed method is improved from a famous graph-based algorithm named Optimum-Path Forest (OPF). Our principal concept of reducing the training set's size is to utilize the Segmented Least Square Algorithm (SLSA) in estimating the tree's shape. From the experimental results, our proposed method could reduce size of the training set by about 7 to 21 percent comparing with the original OPF algorithm while the classification's accuracy decreased insignificantly by only about 0.2 to 0.5 percent. In addition, for some datasets, our method provided even as same degree of accuracy as of the original OPF algorithm.
Starting Page	1
Ending Page	5
File Size	300908
Page Count	5
File Format	PDF
e-ISBN	9781467378253
DOI	10.1109/ICSEC.2015.7401435
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-23
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Support vector machines Supervised Learning Shape Training Set Size Reduction Graph-based Classification Algorothm Prototypes Vegetation Classification algorithms Testing Optimum-Path Forest
Content Type	Text
Resource Type	Article

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