Optimizing F-Measure with Support Vector Machines

Support vector machines (SVMs) are regularly used for classification of unbalanced data by weighting more heavily the error contribution from the rare class. This heuristic technique is often used to learn classifiers with high F-measure, although this particular application of SVMs has not been rigorously examined. We provide significant and new theoretical results that support this popular heuristic. Specifically, we demonstrate that with the right parameter settings SVMs approximately optimize F-measure in the same way that SVMs have already been known to approximately optimize accuracy. This finding has a number of theoretical and practical implications for using SVMs in F-measure optimization.

[1]  Olvi L. Mangasarian,et al.  Hybrid misclassification minimization , 1996, Adv. Comput. Math..

[2]  Olvi L. Mangasarian,et al.  Misclassification minimization , 1994, J. Glob. Optim..

[3]  Bernhard Schölkopf,et al.  The Kernel Trick for Distances , 2000, NIPS.

[4]  Katharina Morik,et al.  Combining Statistical Learning with a Knowledge-Based Approach - A Case Study in Intensive Care Monitoring , 1999, ICML.

[5]  Ramesh C. Agarwal,et al.  PNrule: A New Framework for Learning Classifier Models in Data Mining (A Case-Study in Network Intrusion Detection) , 2001, SDM.

[6]  Vipin Kumar,et al.  Mining needle in a haystack: classifying rare classes via two-phase rule induction , 2001, SIGMOD '01.

[7]  Olvi L. Mangasarian,et al.  Nonlinear Programming , 1969 .

[8]  Vipin Kumar,et al.  Predicting rare classes: can boosting make any weak learner strong? , 2002, KDD.

[9]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[10]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[11]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[12]  Thorsten Joachims,et al.  Making large-scale support vector machine learning practical , 1999 .

[13]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[14]  David R. Musicant,et al.  Lagrangian Support Vector Machines , 2001, J. Mach. Learn. Res..

[15]  Samy Bengio,et al.  SVMTorch: Support Vector Machines for Large-Scale Regression Problems , 2001, J. Mach. Learn. Res..

[16]  David R. Musicant,et al.  Successive overrelaxation for support vector machines , 1999, IEEE Trans. Neural Networks.

[17]  Olvi L. Mangasarian,et al.  Machine Learning via Polyhedral Concave Minimization , 1996 .