A single interval based classifier

Abstract

In many applications, it is desirable to build a classifier that is bounded within an interval. Our motivating example is rooted in monitoring in a stamping process. A novel approach is proposed and examined in this paper. Our method consists of three stages: (1) A baseline of each class is estimated via convex optimization; (2) An “optimal interval” that maximizes the difference among the baselines is identified; (3) A classifier that is based on the “optimal interval” is constructed. We analyze the implementation strategy and properties of the derived algorithm. The derived classifier is named an interval based classifier (IBC) and can be computed via a low-order-of-complexity algorithm. Comparing to existing state-of-the-art classifiers, we illustrate the advantages of our approach. To showcase its usage in applications, we apply the IBC to a set of tonnage curves from stamping processes, and observed superior performance. This method can help identifying faulty situations in manufacturing. The computational steps of IBC take advantage of operations-research methodology. IBC can serve as a general data mining tool, when the features are based on single intervals.