Model Decision Forest Algorithm

Abstract

Random forest (RF) has been widely used in machine learning because of its strong anti-noise ability, high prediction accuracy, and applicability for high-dimensional data. Model decision tree (MDT) is an accelerated decision tree algorithm. Although it can improve the training efficiency of the algorithm, the accuracy of MDT decreases with the increase of impure pseudo leaf nodes size. To solve this problem, model decision forest (MDF) algorithm is proposed to improve the classification accuracy of the MDT. The MDF algorithm takes the MDT as the base classifier and uses the idea of random forest to generate multiple model decision trees. Firstly, the algorithm obtains different sample subsets via rotation matrix. Secondly, multiple different model decision trees are trained on these sample subsets, and integrated through voting. Finally, the classification results will be achieved by the obtained model decision forest. Experimental results on benchmark datasets show that the proposed MDF algorithm is superior to the MDT algorithm in terms of accuracy. Moreover, MDF can obtain high accuracy when the number of trees is small, avoiding the problem of increasing time complexity due to the increment of trees.

Keywords

• gini index
• model decision forest (mdf)
• model decision tree (mdt)
• random forest (rf)