OPTIMIZED POLYNOMIAL FUZZY SWARM NET FOR CLASSIFICATION - Knowledge Mining Using Intelligent Agents - page 259

Chapter 9
OPTIMIZED POLYNOMIAL FUZZY SWARM NET
FOR CLASSIFICATION
B. B. MISRA ∗ ,P.K.DASH † and G. PANDA ‡
∗ Department of Information Technology,
Silicon Institute of Technology,
Bhubaneswar-751024, Orissa, India
[e-mail]
† Multidisciplinary Research Cell,
SOA University,
Bhubaneswar-751030, Orissa, India
pkdash [e-mail]
‡ School of Electrical Sciences,
Indian Institute of Technology,
Bhubaneswar, Orissa, India
[e-mail]
This chapter presents a hybrid approach for solving classification problems.
We have used three important neuro and evolutionary computing techniques
such as Polynomial Neural Network, Fuzzy system, and Particle Swarm
Optimization to design a classifier. The objective of designing such a classifier
model is to overcome some of the drawbacks in the existing systems and to
obtain a model that consumes less time in developing the classifier model, to
give better classification accuracy, to select the optimal set of features required
for designing the classifier and to discard less important and redundant features
from consideration. Over and above the model remains comprehensive and easy
to understand by the users.
9.1. Introduction
The classification task of data mining and knowledge discovery has received
much attention in recent years and is growing very fast. In addition
to classification task of data mining, there exist some more tasks like
association rule mining, clustering, dependency modeling, etc., in data
mining area. However, classification is a fundamental activity of data
mining. Given predetermined disjoint target classes C 1 ,C 2 ,...,Cn ,asetof
259