NDLI: Feature Selection for Classification Using Decision Tree

Content Provider	IEEE Xplore Digital Library
Author	Tahir, N.M. Hussain, A. Samad, S.A. Ishak, K.A. Halim, R.A.
Copyright Year	2006
Description	Author affiliation: Signal Process. Res. Group, Univ. Kebangsaan Malaysia, Bangi (Tahir, N.M.; Hussain, A.; Samad, S.A.; Ishak, K.A.)
Abstract	In most pattern recognition (PR) system, selecting the best feature vectors is an important task. Feature vectors serve as a reduced representation of the original data/signal/input that helps avoid the curse of dimensionality in a PR task. In this work, we consider further effort in selecting the best feature vectors for the PR task. A framework to determine the best eigenvectors of two main human postures based on the rules of thumb of principal component analysis (PCA) has been developed, which includes the KG-rule, cumulative variance and the scree test. Accordingly, two rules of thumb suggest in retaining only the first six eigenvectors or also known as 'eigenposture', as inputs for classification. Using decision tree (DT) as our classifier, two distinct processes need to be implemented namely, building the tree and then performing classification using the top-down approach in DT construction. At each node, decision to what is likely to be the best split is determined using the predicted value. Each branch in the tree is labeled with its decision rule whilst each terminal node is labeled with the predicted value of that node. Cross validation ensures selection of an optimum tree size and helps avoid the problem of over fitting. Consequently, the framework has enabled us to select the best and most optimized eigenpostures for classification purpose.
Starting Page	99
Ending Page	102
File Size	225677
Page Count	4
File Format	PDF
ISBN	9781424405268
DOI	10.1109/SCORED.2006.4339317
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-27
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision Tree Thumb Humans Human Posture Pattern recognition Research and development Classification Signal processing Feature extraction Systems engineering and theory Decision trees Principal Component Analysis Classification tree analysis Principal component analysis
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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