The Computational Efficiency of the Distribution-Based Algotithm
Siddhartha Rao, CHNNA BABU GALINKI, HEMALATA KONDALA · International Journal of Scientific Research · 2012
Classification is a classical problem in machine learning and data mining. One of the most popular classification models is the decision tree model. Many algorithms have been devised for decision tree construction. In traditional decision-tree classification, a feature (an attribute) of a tuple is either categorical or numerical. For the latter, a precise and definite point value is usually assumed. In many applications, however, data uncertainty is common. The value of a feature/attribute is thus best captured not by a single point value, but by a range of values giving rise to a probability distribution. A simple way to handle data uncertainty is to abstract probability distributions by summary statistics such as means and variances. This approach is known as Averaging. Another approach is to consider the complete information carried by the probability distributions to build a decision tree. This approach is known as Distribution-based. The Computational Efficiency of the Distribution-Based Algotithm