NDLI: Improved feature selection based on scatter degree

Content Provider	IEEE Xplore Digital Library
Author	Zare, A. Fouladi, S.H.
Copyright Year	2011
Description	Author affiliation: Department of computing, Faculty of Electrical Engineering, Amirkabir University, Tehran, Iran (Fouladi, S.H.) \|\| Department of electronic engineering, Faculty of Electrical Engineering, Amirkabir University, Tehran, Iran (Zare, A.)
Abstract	Feature reduction is important in machine learning, data mining and pattern recognition fields. Feature reduction consists of two methods: 1. Feature Extraction 2. Feature Selection. Feature selection methods try to select feature subset from feature set. Thus high dimension documents are projected on to lower dimension documents. The goal is selection of best subset that causes minimum error in classification. Scatter degree is one of the feature selection methods which attributes a degree of scattering for each feature. Features are selected that have higher scatter degree. In this paper, classification error has been reduced by considering other aspects in computing scatter degree (Improved Scatter Degree). Obtained results from this method have been compared with Scatter degree method.
Starting Page	97
Ending Page	101
File Size	225238
Page Count	5
File Format	PDF
ISBN	9781457703058
ISSN	21601348
e-ISBN	9781457703089
DOI	10.1109/ICMLC.2011.6016668
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Mathematical model Equations Machine learning Algorithm design and analysis Cybernetics Principal component analysis Pattern recognition Scatter degree Feature selection
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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