A sequential three-way approach to multi-class decision

Based on the notion of information and knowledge granularity, sequential three-way decisions can be interpreted and implemented as a multilevel trisecting-and-acting framework which leads to a faster decision-making with the less cost of decision process. At each level, objects are divided into the triplet, consisting of three pair-wise disjoint regions. By considering different combinations of regions, the uncertain objects should be further investigated at the next level due to insufficient information. In fact, sequential strategy is an effective idea to deal with multi-class problem in granular computing. On the basis of this recognition, we focus on a sequential three-way approach to multi-class decision in this paper. To solve the issues of unequal cost and rule conflict simultaneously, we present a novel matrix-based multi-class decision method based on Bayesian decision procedure. Subsequently, a sequence of the proposed multi-class models is conducted under a multilevel granular structure, and objects are assigned into one certain decision class gradually from the lower levels to the higher levels via a top-down manner. Finally, the results of empirical study involving six multi-class data sets on UCI are reported to validate the feasibility and effectiveness of our proposal.