SURVEY ON SEMI SUPERVISED CLASSIFICATION METHODS AND FEATURE SELECTION

Abstract

Data mining also called knowledge discovery is a pr ocess of analyzing data from several perspective a nd summarize it into useful information. It has tremendous application in the area of classification like pattern recognition, discovering several disease type, analysis of medical image, recognizing speech , for identifying biometric, drug discovery etc. Th is is a survey based on several semisupervised classification method used by classifiers , in this both labeled and unlabeled data can be used for classification purpose.It is less expensive than ot her classification methods . Different techniques surveyed in this paper are low density separation approach, transductive SVM, semi -supervised based logistic discriminate procedure , self training nearest neighbour rule using cut edges, self training neare st neighbour rule using cut edges. Along with class ification methods a review about various feature selection methods is also men tioned in this paper. Feature selection is performe d to reduce the dimension of large dataset. After reducing attribute the data is given for classification hence the accuracy and pe rformance of classification system can be improved .Several feature selection m ethod include consistency based feature selection, fuzzy entropy measure feature selection with similarity classifier, Signal to noise ratio,Positive approximation. So ea ch method has several benefits. Index Terms: Semisupervised classification, Transductive support vector machine, Feature selection, unlabeled sampl es