抽象的

A SURVEY-CLASSIFIER FUSION

Kanchan Saxena and Vineet Richaria

A number of classifier fusion methods have been recently developed opening an alternative approach leading to a potential improvement in the classification performance. As there is little theory of information fusion itself, currently we are faced with different methods designed for different problems and producing different results.This paper presents the survey of various classification technique for getting the optimal result applying with fusion technique. Classification is the example of supervised learning. Classification is a data mining function that assigns items in a collection to target categories or classes. The goal of classification is to accurately predict the target class for each case in the data .many classification technique are used for improving the accuracy of the classifier such as k-nearest neighbor (knn), support vector machine (svm), clustering etc. The growth of rate of data increses in current decade. The internet genrate huge amount of unstuctured data, the whole data contains of text, document, vedio and image. The gropping of data required the classification .The classification as a part of supervised learning, in this technique the gropping of data occur in a guided fashion. We insentively review various research and journal paper related to data classification used such different methodology such technique are knn(k-nearest neighbour), svm(support vector machine), clustering and classification. In recent research data mining evolved a new emerging technique such a technique is called DATA FUSION.