NDLI: Making Diversity Enhancement Based on Multiple Classifier System by Weight Tuning

Content Provider	Springer Nature Link
Author	Salkhordeh Haghighi, Mehdi Vahedian, Abedin Sadoghi Yazdi, Hadi
Copyright Year	2011
Abstract	This article presents a new method to construct multiple classifier system by making diverse base classifiers using weight tuning. In the method presented, base classifiers are multilayer perceptions which creates diverse base classifiers using a three-step procedure. In the first step, base classifiers are trained for acceptable accuracy. In the second step, a weight tuning process tunes their weights such that each one can distinguish one class of input data from the others with highest possible accuracy. An evolutionary method is used to optimize efficiency of each base classifier to distinguish one class of input data in this step. In the third step, a new method combines the results of the base classifiers. As diversity is measured and monitored throughout the entire procedure, it is measured using a confusion matrix. Superiority of the proposed method is discussed using several known classifier fusion methods and known benchmark datasets.
Starting Page	61
Ending Page	80
Page Count	20
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	35
Issue Number	1
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2011-11-09
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	Combining classifiers Classifier fusion Classifier diversity Multiple classifier system diversity Computational Intelligence Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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

Kernel Local Sparse Representation Based Classifier

A Novel Weightless Artificial Neural Based Multi-Classifier for Complex Classifications

Multiple classifier systems for robust classifier design in adversarial environments

Feature Fusion Using Multiple Component Analysis

An Efficient and Effective Multiple Empirical Kernel Learning Based on Random Projection

Extension of a Kernel-Based Classifier for Discriminative Spoken Keyword Spotting

Erratum to: A Method for Metric Learning with Multiple-Kernel Embedding

On the Design of Robust Linear Pattern Classifiers Based on $$M$$ -Estimators

Online Training for Open Faulty RBF Networks

Making Diversity Enhancement Based on Multiple Classifier System by Weight Tuning

Similar Documents

Kernel Local Sparse Representation Based Classifier

A Novel Weightless Artificial Neural Based Multi-Classifier for Complex Classifications

Multiple classifier systems for robust classifier design in adversarial environments

Feature Fusion Using Multiple Component Analysis

An Efficient and Effective Multiple Empirical Kernel Learning Based on Random Projection

Extension of a Kernel-Based Classifier for Discriminative Spoken Keyword Spotting

Erratum to: A Method for Metric Learning with Multiple-Kernel Embedding

On the Design of Robust Linear Pattern Classifiers Based on $$M$$ -Estimators

Online Training for Open Faulty RBF Networks

Making Diversity Enhancement Based on Multiple Classifier System by Weight Tuning