NDLI: A Pattern Classifying System Based on the Coverage Regions of Objects

Content Provider	IEEE Xplore Digital Library
Author	Suzuki, I.
Copyright Year	2011
Abstract	A new statistical pattern classifying system is proposed to solve the problem of the "peaking phenomenon". In this phenomenon, the accuracy of a pattern classifier peaks as the features increase under a fixed size of training samples. Instead of estimating the distribution of class objects, the system generates a region on the feature space, in which a certain rate of class objects is included. The pattern classifier identifies the class if the object belongs to only one class of the coverage region, but answers "unable to detect" if the object belongs to the coverage region of more than one class or belongs to none. Here, the coverage region is simply produced from the coverage regions of each feature and then extended if necessary. Unlike the Naive-Bayes classifier, the independence of each feature is not assumed. In tests of the system on the classification of characters, the performance does not significantly decrease as the features increase unless apparently useless features are added.
Starting Page	401
Ending Page	405
File Size	215459
Page Count	5
File Format	PDF
ISBN	9781457721342
DOI	10.1109/ICMLA.2011.20
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Accuracy Bayesian methods feature extraction error rate supervised learning peaking phenomenon Vectors Indexes Naive Bayes classifier Optimization Sorting
Content Type	Text
Resource Type	Article

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