ABSTRACT
An enhanced fuzzy min–max (EFMM) network is proposed for pattern classification in this paper.
The aim is to overcome a number of limitations of the original fuzzy min–max (FMM) network and improve its classification performance. The key contributions are three heuristic rules to enhance the learning algorithm of FMM. First, a new hyperbox expansion rule to eliminate the overlapping problem during the hyperbox expansion process is suggested. Second, the existing hyperbox overlap test rule is extended to discover other possible overlapping cases. Third, a new hyperbox contraction rule to resolve possible overlapping cases is provided. Efficacy of EFMM is evaluated using benchmark data sets and a real medical diagnosis task. The results are better than those from various FMM based models, support vector machine-based, Bayesian-based, decision tree-based, fuzzy-based, and neural-based classifiers. The empirical findings show that the newly introduced rules are able to realize EFMM as a useful model for undertaking pattern classification problems.
Further Details Contact: A Vinay 9030333433, 08772261612
Email: info@takeoffprojects.com | www.takeoffprojects.com

EXISTING SYSTEM
A number of limitations of the original fuzzy min–max (FMM) network and improve its classification performance. The key contributions are three heuristic rules to enhance the learning algorithm of FMM. First, a new hyperbox expansion rule to eliminate the overlapping problem during the hyperbox expansion process is suggested. Second, the existing hyperbox overlap test rule is extended to discover other possible overlapping cases. Third, a new hyperbox contraction rule to resolve possible overlapping cases is provided.
PROPOSED SYSTEM
An enhanced fuzzy min–max (EFMM) network is proposed for pattern classification in this paper.
Efficacy of EFMM is evaluated using benchmark data sets and a real medical diagnosis task. The results are better than those from various FMM based models, support vector machine-based,
Bayesian-based, decision tree-based, fuzzy-based, and neural-based classifiers. The empirical findings show that the newly introduced rules are able to realize EFMM as a useful model for undertaking pattern classification problems.
Further Details Contact: A Vinay 9030333433, 08772261612
Email: info@takeoffprojects.com | www.takeoffprojects.com

SYSTEM CONFIGURATION:-
HARDWARE CONFIGURATION:-

Processor

Speed

RAM

Hard Disk

Key Board

Mouse

Monitor
-
-
-
-
-
Pentium –IV
1.1 Ghz
- Two or Three Button Mouse
-
256 MB(min)
20 GB
Standard Windows Keyboard
SVGA
SOFTWARE CONFIGURATION:-

Operating System

Programming Language

Java Version

Database
: Windows XP
: JAVA/J2EE
: JDK 1.6 & above.
: MYSQL
Further Details Contact: A Vinay 9030333433, 08772261612
Email: info@takeoffprojects.com | www.takeoffprojects.com