Uncertain Data Classification Using Decision Tree Classification Tool With Probability Density Function Modeling Technique

摘要

Classical decision tree classifiers are constructed using certain or point data only. But in many real life applications inherently data is always uncertain. Attribute or value uncertainty is inherently associated with data values during data collection process. Attributes in the training data sets are of two types ¨Cnumerical (continuous) and categorical (discrete) attributes. Data uncertainty exists in both numerical and categorical attributes.Datauncertainty in numerical attributes means range of values and data uncertainty in categorical attributes means set or collection of values. In this paper we propose a method for handling data uncertainty in numerical attributes. One of the simplest and easiest methods of handling data uncertainty in numerical attributes is finding the mean or average or representative value of the set of original values of each value of an attribute. With data uncertainty the value of an attribute is usually represented by a set of values. Decision tree classification accuracy is much improved when attribute values are represented by sets of values rather than one single representative value. Probability density function with equal probabilities is one effective data uncertainty modellingtechnique to represent each value of an attribute as a set of values. Here the main assumption is that actual values provided in the training data sets are averaged or representative values of originally collected values through data collection process. For each representative value of each numerical attribute in the training data set, approximated values corresponding to the originally collected values are generated by using probability density function with equal probabilities and these newly generated sets of values are used in constructing a new decision tree classifier.