Rank forest: Systematic attribute sub-spacing in decision forest

Decision Trees are well known classification algorithms that are also appreciated for their capacity for knowledge discovery. In the literature two major shortcomings of decision trees have been pointed out: (1) instability, and (2) high computational cost. These problems have been addressed to some extent through ensemble learning techniques such as Random Forest. Unlike decision trees where the whole attribute space of a dataset is used to discover the best test attribute for a node, in Random Forest a random subspace of attributes is first selected from which the test attribute for a node is then identified. The property that randomly selects an attribute subspace can cause the selection of all/many poor quality attributes in a subspace resulting in an individual tree with low accuracy. Therefore, in this paper we propose a probabilistic selection of attributes (instead of a random selection) where the probability of the selection of an attribute is proportionate to its quality. Although we developed this approach independently, after the research was completed we discovered that some existing techniques also took the same approach. While in this paper we use mutual information as a measure of an attribute quality, the papers in the literature used information gain ratio and a t-test as the measure. The proposed technique has been evaluated using nine different datasets and a stable performance can be seen in terms of the accuracy (ensemble accuracy and individual tree accuracy) and efficiency.