International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
Decision trees are considered to be one of the most popular approaches for representing classifiers. Researchers from various disciplines such as statistics, machine learning, pattern recognition and data mining have dealt with the issue of growing a decision tree from available data. Traditional decision tree classifiers used to work on the data whose values are known and precise. Therefore in this paper, such classifiers are used to handle data with uncertain information. Two approaches has been implemented one is the averaging in which the data uncertainty is represented by abstract probability distribution by summary statistics such as mean and variances.