Multi-agent classifier system based on heterogeneous classifier

The MAS model consists of several independents agents, and these agents has the ability to carry out a specific task and to make decisions. When working, these agents will share information with each other. Indirectly, this allows the system to get better predictions. When the constituent agents in...

Full description

Bibliographic Details
Main Author:	Nur Afiqah, Mohd Dali
Format:	Undergraduates Project Papers
Language:	English
Published:	2018
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/26938/ http://umpir.ump.edu.my/id/eprint/26938/ http://umpir.ump.edu.my/id/eprint/26938/1/Multi-agent%20classifier%20system%20based%20on%20heterogeneous.pdf

id	ump-26938
recordtype	eprints
spelling	ump-269382019-12-17T03:02:00Z http://umpir.ump.edu.my/id/eprint/26938/ Multi-agent classifier system based on heterogeneous classifier Nur Afiqah, Mohd Dali QA75 Electronic computers. Computer science QA76 Computer software The MAS model consists of several independents agents, and these agents has the ability to carry out a specific task and to make decisions. When working, these agents will share information with each other. Indirectly, this allows the system to get better predictions. When the constituent agents in a MAS model consist of classifiers, the resulting system is known as a multi-agent classifier system (MACS). In this project, our focus is about mutli-agent classifier system based on heterogeneous classifiers. This is because based on the previous analysis, previous MACS model that used homogeneous type of classifiers i.e., FMMs or EFMM have problem with noise effect and noise tolerance, where both classifiers have no mutant against noise. That could have a negative effect on the classification performance. In fact, learning with noise data can cause false knowledge which will be represented as noisy hyperbox in the topology of the classifier. In order to solve this problem we propose to use a heterogeneous classifiers with pruning strategy that have the ability to reduce noise effects. That could improve the MACS classification performance by overcomes the limitations of each classifier when handling different classification problems. 2018-12 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/26938/1/Multi-agent%20classifier%20system%20based%20on%20heterogeneous.pdf Nur Afiqah, Mohd Dali (2018) Multi-agent classifier system based on heterogeneous classifier. Faculty of Computer System & Software Engineering, Universiti Malaysia Pahang. http://fypro.ump.edu.my/ethesis/index.php
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science QA76 Computer software
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software Nur Afiqah, Mohd Dali Multi-agent classifier system based on heterogeneous classifier
description	The MAS model consists of several independents agents, and these agents has the ability to carry out a specific task and to make decisions. When working, these agents will share information with each other. Indirectly, this allows the system to get better predictions. When the constituent agents in a MAS model consist of classifiers, the resulting system is known as a multi-agent classifier system (MACS). In this project, our focus is about mutli-agent classifier system based on heterogeneous classifiers. This is because based on the previous analysis, previous MACS model that used homogeneous type of classifiers i.e., FMMs or EFMM have problem with noise effect and noise tolerance, where both classifiers have no mutant against noise. That could have a negative effect on the classification performance. In fact, learning with noise data can cause false knowledge which will be represented as noisy hyperbox in the topology of the classifier. In order to solve this problem we propose to use a heterogeneous classifiers with pruning strategy that have the ability to reduce noise effects. That could improve the MACS classification performance by overcomes the limitations of each classifier when handling different classification problems.
format	Undergraduates Project Papers
author	Nur Afiqah, Mohd Dali
author_facet	Nur Afiqah, Mohd Dali
author_sort	Nur Afiqah, Mohd Dali
title	Multi-agent classifier system based on heterogeneous classifier
title_short	Multi-agent classifier system based on heterogeneous classifier
title_full	Multi-agent classifier system based on heterogeneous classifier
title_fullStr	Multi-agent classifier system based on heterogeneous classifier
title_full_unstemmed	Multi-agent classifier system based on heterogeneous classifier
title_sort	multi-agent classifier system based on heterogeneous classifier
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/26938/ http://umpir.ump.edu.my/id/eprint/26938/ http://umpir.ump.edu.my/id/eprint/26938/1/Multi-agent%20classifier%20system%20based%20on%20heterogeneous.pdf
first_indexed	2023-09-18T22:42:15Z
last_indexed	2023-09-18T22:42:15Z
_version_	1777416992895008768

Multi-agent classifier system based on heterogeneous classifier

Similar Items