Empirical Comparison of Sampling Strategies for Classification

AbstractData sets contain very large amount of data which is not an easy task for the user to scan the entire data set. The researcher's initial task is to formulate a rational justification for the use of sampling in his research. Sampling has been often suggested as an effective tool to reduce the size of the dataset operated at some cost to accuracy. It is the process of selecting representatives which indicates the complete data set by examining a fraction. Due to sampling we overcome the problems like; i) in research it is not possible to collect and test each and every element from the data base individually; and ii) study of sample rather than the entire dataset is also sometimes likely to produce more reliable results. This paper focuses on different types of sampling strategies applied on neural network. Here sampling technique has been applied on two real, integers and categorical dataset such as yeast and hepatitis data set prior to classification. The main objective of this paper is an empirical comparison of different sampling strategies for classification which gives more accuracy