NDLI: Performance results of recognizing various class types using classifier decision fusion

Content Provider	IEEE Xplore Digital Library
Author	Lynch Jr., R.S.
Copyright Year	2002
Description	Author affiliation: Signal Process. Branch, Naval Undersea Warfare Center, Newport, RI, USA (Lynch, R.S., Jr.)
Abstract	Classification performance is compared using data based on waveforms that are transmitted at both low and high frequencies. The waveforms are made up of one low, and one high, bandwidth type. Various features have been extracted from each waveform for training different classifiers. Specifically, the classifier types consist of various neural networks, Weighted voting (Linear), Fisher's Linear discriminant, the Expectation Maximization algorithm, and the Bayesian Data Reduction Algorithm. The contribution of this paper is to show that overall classification performance improves if the decision outputs of the individually trained classifiers are fused using majority voting. Also, it is shown that classification performance depends on the transmitting carrier frequency of the waveforms and the specific configuration of the classes used to train each classifier.
Starting Page	266
Ending Page	271
File Size	600993
Page Count	6
File Format	PDF
ISBN	0972184414
DOI	10.1109/ICIF.2002.1021160
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-07-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Int. Soc. of Information Fusion
Subject Keyword	Testing Frequency Voting Bandwidth Feature extraction Data mining Signal processing algorithms Probability Training data Signal processing
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

Classifier fusion results using various open literature data sets

Improving knowledge extraction of Hadith classifier using decision tree algorithm

Automatic credit card fraud detection based on non-linear signal processing

Prediction of Cancer Class with Majority Voting Genetic Programming Classifier Using Gene Expression Data

Enhancing feature extraction for VF detection using data mining techniques

Feature extraction in acoustic signals using the BCM learning rule

Feature-based multisensor fusion and performance quantification using sufficient statistics

A Feature Weighted Ensemble Classifier on Stream Data

Highly accurate palmprint recognition using statistical and wavelet features

Performance results of recognizing various class types using classifier decision fusion

Similar Documents

Classifier fusion results using various open literature data sets

Improving knowledge extraction of Hadith classifier using decision tree algorithm

Automatic credit card fraud detection based on non-linear signal processing

Prediction of Cancer Class with Majority Voting Genetic Programming Classifier Using Gene Expression Data

Enhancing feature extraction for VF detection using data mining techniques

Feature extraction in acoustic signals using the BCM learning rule

Feature-based multisensor fusion and performance quantification using sufficient statistics

A Feature Weighted Ensemble Classifier on Stream Data

Highly accurate palmprint recognition using statistical and wavelet features

Performance results of recognizing various class types using classifier decision fusion