Fusing Complete Monotonic Decision Trees

Abstract

Monotonic classification is a kind of classification task in which a monotonicity constraint exist between features and class, i.e., if sample $x_i$ has a higher value in each feature than sample $x_j$ , it should be assigned to a class with a higher level than the level of $x_j$ 's class. Several methods have been proposed, but they have some limits such as with limited kind of data or limited classification accuracy. In our former work, the classification accuracy on monotonic classification has been improved by fusing monotonic decision trees, but it always has a complex classification model. This work aims to find a monotonic classifier to process both nominal and numeric data by fusing complete monotonic decision trees. Through finding the completed feature subsets based on discernibility matrix on ordinal dataset, a set of monotonic decision trees can be obtained directly and automatically, on which the rank is still preserved. Fewer decision trees are needed, which will serve as base classifiers to construct a decision forest fused complete monotonic decision trees. The experiment results on 10 datasets demonstrate that the proposed method can reduce the number of base classifiers effectively and then simplify classification model, and obtain good classification performance simultaneously.