E3WD: A three-way decision model based on ensemble learning

Abstract

Three-way decision model is an effective way to deal with complex decision problems. However, since the three-way decision models now proposed are all based on a single decision criterion, the decision results typically reflect only one preference of decision-makers. Thus, these models may also not effectively deal with complex decision-making problems. To solve the above problems, this paper proposes a new three-way decision model based on ensemble learning. Specifically, we first obtain different three-way decision results by employing different decision criteria. Then, we can acquire the core and candidate sets of the positive and negative regions through set operations. Next, we use the K-means algorithm to divide the candidate sets into three disjoint subsets based on similarities. After that, we adopt a hierarchical filtering method to select suitable objects from the candidate sets and add them to the core sets. Finally, we employ four three-way decision models with different decision criteria as examples to conduct experiments on eight datasets. Experimental results show that our proposed model can obtain higher classification accuracy and lower deferment rate than other traditional three-way decision models under most experimental conditions.