In practical applications, machine learning algorithms are often needed to learn classifiers that optimize domain specific performance measures. Previously, the research has focused on learning the needed classifier in isolation, yet learning nonlinear classifier for nonlinear and nonsmooth performance measures is still hard. In this paper, rather than learning the needed classifier by optimizing specific performance measure directly, we circumvent this problem by proposing a novel two-step approach called CAPO, namely, to first train nonlinear auxiliary classifiers with existing learning methods and then to adapt auxiliary classifiers for specific performance measures. In the first step, auxiliary classifiers can be obtained efficiently by taking off-the-shelf learning algorithms. For the second step, we show that the classifier adaptation problem can be reduced to a quadratic program problem, which is similar to linear @math and can be efficiently solved. By exploiting nonlinear auxiliary classifiers, CAPO can generate nonlinear classifier which optimizes a large variety of performance measures, including all the performance measures based on the contingency table and AUC, while keeping high computational efficiency. Empirical studies show that CAPO is effective and of high computational efficiency, and it is even more efficient than linear @math .
@cite_7: This paper presents a Support Vector Method for optimizing multivariate nonlinear performance measures like the F 1 -score. Taking a multivariate prediction approach, we give an algorithm with which such multivariate SVMs can be trained in polynomial time for large classes of potentially non-linear performance measures, in particular ROCArea and all measures that can be computed from the contingency table. The conventional classification SVM arises as a special case of our method.
The most famous work that optimizes performance measures is @cite_7 . By taking a multivariate prediction formulation, it finds the classifier in the function space directly. Our proposed CAPO works in a different manner and employs auxiliary classifiers to help find the target classifier in the function space. Furthermore, CAPO is a framework that can use different types of auxiliary classifiers. If nonlinear auxiliary classifier is used, the obtained classifier will also be nonlinear. This is very helpful, because nonlinear classifier is preferred in many applications while training nonlinear is computationally expensive. In summary, compared with , CAPO can provide the needed nonlinearity whilst keeping even improving computational efficiency.