NDLI: Learning to classify by ongoing feature selection

Content Provider	IEEE Xplore Digital Library
Author	Levi, D. Ullman, S.
Copyright Year	2006
Description	Author affiliation: Weizmann Institute of Science, Rehovot, Israel (Levi, D.)
Abstract	Existing classification algorithms use a set of training examples to select classification features, which are then used for all future applications of the classifier. A major problem with this approach is the selection of a training set: a small set will result in reduced performance, and a large set will require extensive training. In addition, class appearance may change over time requiring an adaptive classification system. In this paper we propose a solution to these basic problems by developing an on-line feature selection method, which continuously modifies and improves the features used for classification based on the examples provided so far. The method is used for learning a new class, and to continuously improve classification performance as new data becomes available. In ongoing learning, examples are continuously presented to the system, and new features arise from these examples. The method continuously measures the value of the selected features using mutual information, and uses these values to efficiently update the set of selected features when new training information becomes available. The problem is challenging because at each stage the training process uses a small subset of the training data. Surprisingly, with sufficient training data the on-line process reaches the same performance as a scheme that has a complete access to the entire training data.
Starting Page	1
Ending Page	1
File Size	700817
Page Count	1
File Format	PDF
ISBN	0769525423
DOI	10.1109/CRV.2006.46
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-07
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Degradation Adaptive systems Training data Feature extraction Mathematics Classification algorithms Application software Data mining Mutual information
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 Library 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

Sentiment classification in Persian: Introducing a mutual information-based method for feature selection

Comparison of feature selection algorithms for medical data

Online Feature Selection and Its Applications

Multi-Inductive Learning approach for Information Extraction

Using the maximum Mutual Information criterion to textural Feature Selection for satellite image classification

A Novel Hypothesis-Margin Based Method Incorporating Minimal-Redundancy Criterion for Feature Selection

A Two-step Feature Selection Algorithm Adapting to Intrusion Detection

Improved feature selection approach TFIDF in text mining

Gait Feature Subset Selection by Mutual Information

Learning to classify by ongoing feature selection

Similar Documents

Sentiment classification in Persian: Introducing a mutual information-based method for feature selection

Comparison of feature selection algorithms for medical data

Online Feature Selection and Its Applications

Multi-Inductive Learning approach for Information Extraction

Using the maximum Mutual Information criterion to textural Feature Selection for satellite image classification

A Novel Hypothesis-Margin Based Method Incorporating Minimal-Redundancy Criterion for Feature Selection

A Two-step Feature Selection Algorithm Adapting to Intrusion Detection

Improved feature selection approach TFIDF in text mining

Gait Feature Subset Selection by Mutual Information

Learning to classify by ongoing feature selection