A three-way classification with fuzzy decision trees

Abstract

This study is concerned with the design of a three-way classification mechanism realized through combing fuzzy decision trees and expressing uncertainty associated with classification results. The fuzzy decision tree used in this study is constructed through the generalization of the commonly used decision trees. The notion of three-way decision model first proposed for the interpretation of rules generated in rough set approximation has been widely used in many fields. This study proposes an efficient way to flag data with high level of uncertainty with the classification realized by fuzzy decision trees, which is the capability that commonly used fuzzy decision trees do not have. The data identified in this way are left to users’ judgments or more advanced classification techniques. The developed mechanism is formed as a two-stage construct where a fuzzy decision tree is built by generalizing the Boolean classification boundaries of a pre-constructed decision tree using fuzzy sets and then determining the level of uncertainty to identify instances to be rejected due to a lack of sufficient confidence in their belongingness. The rejected instances that are difficult to process are classified as non-commitment cases and left to some further analyses. The rejection quality of the developed three-way classifier is quantified in terms of the classification accuracy and rejection coefficient. We also elaborate on striking a sound tradeoff between these two performance indicators. Experimental studies demonstrate that the developed mechanism could effectively improve the classification accuracy at the cost of a small proportion of the rejected instances and achieve better performance in comparison with other three-way decision models when generating a three-way decision output.