NDLI: Efficient feature selection based on independent component analysis

Content Provider	IEEE Xplore Digital Library
Author	Prasad, M. Sowmya, A. Koch, I.
Copyright Year	2004
Description	Author affiliation: Sch. of Comput. Sci. & Eng., New South Wales Univ., Sydney, NSW, Australia (Prasad, M.)
Abstract	Feature selection, often used as a pre-processing step to machine learning, is designed to reduce dimensionality, eliminate irrelevant data and improve accuracy. In this paper, we introduce a novel approach to reduce dimensionality of the feature space by employing independent component analysis. While ICA is primarily a feature extraction technique, we use it here as a feature selection technique in a generic way. Our technique, called FSS/spl I.bar/ICA, is more efficient than many of its competitors without loss in accuracy. FSS/spl I.bar/ICA determines a set of statistically independent features instead of merely reducing the number of the original features. In applications FSS_ICA results in a smaller number of effective features than the relief attribute estimator, and it usually outperforms both the relief attribute estimator and CFS, when used as a pre-processing step for naive Bayes, instance based learning and decision trees. In addition, by disregarding some features, we demonstrate that in some cases FSS_ICA is more accurate than classification based on all features. Also, decision trees built from the pre-processed data are often significantly smaller than those derived from the original feature space. In addition, we also report the performance of ICA on a "real world" application in medical image segmentation.
Starting Page	427
Ending Page	432
File Size	924248
Page Count	6
File Format	PDF
ISBN	0780388941
DOI	10.1109/ISSNIP.2004.1417499
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-12-14
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Design engineering Filters Independent component analysis Machine learning Feature extraction Data engineering Linear discriminant analysis Decision trees 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

Discriminant Independent Component Analysis

Building Efficient Intrusion Detection Model Based on Principal Component Analysis and C4.5

Synthesizing Novel Dimension Reduction Algorithms in Matrix Trace Oriented Optimization Framework

Personal authentication based on iris texture analysis

Feature Extraction Using Recursive Cluster-Based Linear Discriminant With Application to Face Recognition

Comparison of feature selection approaches based on the SVM classification

Feature subset selection using ICA for classifying emphysema in HRCT images

Feature selection based on scatter degree

On Feature Extraction via Kernels

Efficient feature selection based on independent component analysis

Similar Documents

Discriminant Independent Component Analysis

Building Efficient Intrusion Detection Model Based on Principal Component Analysis and C4.5

Synthesizing Novel Dimension Reduction Algorithms in Matrix Trace Oriented Optimization Framework

Personal authentication based on iris texture analysis

Feature Extraction Using Recursive Cluster-Based Linear Discriminant With Application to Face Recognition

Comparison of feature selection approaches based on the SVM classification

Feature subset selection using ICA for classifying emphysema in HRCT images

Feature selection based on scatter degree

On Feature Extraction via Kernels

Efficient feature selection based on independent component analysis