Classification for overlapping classes using optimized overlapping region detection and soft decision

In many real applications such as target detection and classification, there exist severe overlaps between different classes due to various reasons. Traditional classifiers with crisp decision often produce high rates of mis-classifications for patterns in overlapping regions. In this paper, we propose to use soft decision strategy with an optimized overlapping region detection to address the overlapping class problem. In contrast to crisp decision that assigns a single label to a pattern, the soft decision strategy provides multiple decision options to system operators for further analysis, which is believed to be better than producing a wrong classification. The effectiveness of the proposed method has been tested on both artificial and real-world problems.