A Simple Regularized Multiple Criteria Linear Programs for Binary Classification

Optimization is an important tool in computational finance and business intelligence. Multiple criteria mathematical pro- gram(MCMP), which is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously, is one of the ways of utilizing optimization techniques. Due to the existence of multiple objec- tives, MCMPs are usually difficult to be optimized. In fact, for a nontrivial MCMP, there does not exist a single solution that optimizes all the objectives at the same time. In practice, many methods convert the original MCMP into a single-objective program and solve the obtained scalarized optimization problem. If the values of scalarization parameters, which measure the trade-offs between the conflicting objectives, are not chosen carefully, the converted single-objective optimization problem may be not solvable. Therefore, to make sure MCMP always can be solved successfully, heuristic search and expert knowledge for deciding the value of scalarization parameters are always necessary, which is not an easy task and limits the applications of MCMP to some extend. In this paper, we take the multiple criteria linear program(MCLP) for binary classification as the example and discuss how to modified the formulation of MCLP directly to guarantee the solvability. In details, we propose adding a quadratic regularization term into the converted single-objective linear program. The new regularized formulation does not only overcomes some defects of the original scalarized problem in modeling, it also can be shown in theory that the finite optimal solutions always exist. To test the performance of the proposed method, we compare our algorithm with sever- al state-of-the-art algorithms for binary classification on several different kinds of datasets. Preliminary experimental results demonstrate the effectiveness of our regularization method