ABSTRACT

The task of classification with incomplete data is a complex phenomena and its performance depends upon the method selected for handling the missing data. Missing data occur in datasets when no data value is stored for an attribute / feature in the dataset. This paper provides a brief overview to the problem of incomplete data handling techniques and discusses the various methods used with classification and missing data.