Learning from Small Data Set to Build Classification Model: A Survey
Nidhi H. Ruparel, Nitin M. Shahane, Devyani P. Bhamare · 2013
Classification is one of the important data mining techniques. Learning from a given data set to build a classification model becomes difficult when available sample size is small. How to extract more effective information from a small data set is thus of considerable interest. In this paper we provide a review of different classification methods which will help us build more amounts of data, so that classification performance is improved. We discuss different techniques which will work with small data set such as attribute construction, bootstrap method, incremental method and different diffusion functions. Different classification methods such as neural network, decision tree classifiers, Bayesian classifiers etc. are also discussed.