NDLI: A Novel Feature Selection and Extraction Technique for Classification

Content Provider	IEEE Xplore Digital Library
Author	Goel, K. Vohra, R. Bakshi, A.
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci., Rutgers Univ., New Brunswick, NJ, USA (Bakshi, A.) \|\| Dept. of Comput. Sci., BITS Pilani Goa, Pilani, India (Goel, K.) \|\| Dept. of Math., BITS Pilani Goa, Pilani, India (Vohra, R.)
Abstract	Pattern recognition is a vast field which has seen significant advances over the years. As the datasets under consideration grow larger and more comprehensive, using efficient techniques to process them becomes increasingly important. We present a versatile technique for the purpose of feature selection and extraction - Class Dependent Features (CDFs). CDFs identify the features innate to a class and extract them accordingly. The features thus extracted are relevant to the entire class and not just to the individual data item. This paper focuses on using CDFs to improve the accuracy of classification and at the same time control computational expense by tackling the curse of dimensionality. In order to demonstrate the generality of this technique, it is applied to two problem statements which have very little in common with each other - handwritten digit recognition and text categorization. It is found that for both problem statements, the accuracy is comparable to state-of-the-art results and the speed of the operation is considerably greater. Results are presented for Reuters-21578 and Web-KB datasets relating to text categorization and the MNIST and USPS datasets for handwritten digit recognition.
Starting Page	104
Ending Page	109
File Size	206139
Page Count	6
File Format	PDF
ISBN	9781479943357
ISSN	21676445
e-ISBN	9781479943340
DOI	10.1109/ICFHR.2014.25
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-01
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Support vector machines Handwriting recognition Text categorization Vectors Text recognition Accuracy Pattern Recognition MNIST USPS Reuters-21578 WebKB Hand-written Digit Recognition Text Categorization SVM
Content Type	Text
Resource Type	Article

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