NDLI: A methodology to carry out voting classification tasks using a particle swarm optimization-based neuro-fuzzy competitive learning network

Please wait, while we are loading the content...

A methodology to carry out voting classification tasks using a particle swarm optimization-based neuro-fuzzy competitive learning network

Content Provider	Springer Nature Link
Author	Tamvakis, Androniki Tsekouras, George E. Rigos, Anastasios Kalloniatis, Christos Anagnostopoulos, Christos Nikolaos Anastassopoulos, George
Copyright Year	2016
Abstract	The problem being investigated in this paper concerns the generation of an optimal ensemble (i.e. subset) of classifiers (picked up from set of classifiers applied on a specific classification task) that maximizes the classification performance of the voting ensemble method. The design of an algorithmic framework to meet the above goal would benefit the voting process as far as its complexity is concerned. The methodology employed here treats the classifiers as objects represented by binary vectors, and quantifies the dissimilarities between pairs of classifiers. Then, a multidimensional scaling approach is put in place to transform the classifiers into points in a low-dimensional Euclidean space. The set of the resulting points is processed by a neuro-fuzzy competitive learning network trained by a hybrid procedure that combines the merits of fuzzy clustering and particle swarm optimization. The network’s outcome is a set of homogenous groups of classifiers. To this end, the optimal ensemble is obtained by selecting from each group the best classifier. The method was successfully applied to a number of simulation experiments that involved many data sets and classifiers. Comparative analysis with other relative algorithms took place. The results were very promising as the proposed method appeared to be significantly superior in all of the experiments.
Starting Page	49
Ending Page	69
Page Count	21
File Format	PDF
ISSN	18686478
Journal	Evolving Systems
Volume Number	8
Issue Number	1
e-ISSN	18686486
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-06-28
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Voting classification Multidimensional scaling Particle swarm optimization Fuzzy clustering Neuro-fuzzy network Competitive learning Complexity Artificial Intelligence (incl. Robotics) Complex Systems
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation Computer Science Applications

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

Intuitionistic neuro-fuzzy network with evolutionary adaptation

A fuzzy multi-objective particle swarm optimization for effective data clustering

Fuzzy decision function estimation using fuzzified particle swarm optimization

Fuzzy relational clustering based on comparing two proximity matrices with utilization of particle swarm optimization

Dynamic clustering using combinatorial particle swarm optimization

Capacitated two-stage facility location problem with fuzzy costs and demands

Recursive Gath–Geva clustering as a basis for evolving neuro-fuzzy modeling

A robust self-learning PID control system design for nonlinear systems using a particle swarm optimization algorithm

A fast particle swarm optimization for clustering

A methodology to carry out voting classification tasks using a particle swarm optimization-based neuro-fuzzy competitive learning network

Similar Documents

Intuitionistic neuro-fuzzy network with evolutionary adaptation

A fuzzy multi-objective particle swarm optimization for effective data clustering

Fuzzy decision function estimation using fuzzified particle swarm optimization

Fuzzy relational clustering based on comparing two proximity matrices with utilization of particle swarm optimization

Dynamic clustering using combinatorial particle swarm optimization

Capacitated two-stage facility location problem with fuzzy costs and demands

Recursive Gath–Geva clustering as a basis for evolving neuro-fuzzy modeling

A robust self-learning PID control system design for nonlinear systems using a particle swarm optimization algorithm

A fast particle swarm optimization for clustering

A methodology to carry out voting classification tasks using a particle swarm optimization-based neuro-fuzzy competitive learning network