A survey on Improving Classification Accuracy in Data Mining

Abstract

There are various classifiers available for data classification, selecting the best classifier is one of the critical problems of data classification. Also pre-processing approach to be used is quite important. In this paper, study of various approaches to improve the classification accuracy in data mining is carried out. The purpose of the pre-processing is to gain a high degree of distinct classes before the classifier is trained or tested. Handling noise and outliers is an important aspect in data mining to improve the classification accuracy. High accuracy of classification also depends upon the quality of data being used for classification in data mining. Feature selection is also one of the aspects which can refine the dataset before providing it to the learning algorithm to improve the accuracy of the classifier.