Controlling the Sensitivity of Support Vector Machines

For many applications it is important to accurately distinguish false negative results from false positives. This is particularly important for medical diagnosis where the correct balance between sensitivity and specificity plays an important role in evaluating the performance of a classifier. In this paper we discuss two schemes for adjusting the sensitivity and specificity of Support Vector Machines and the description of their performance using receiver operating characteristic (ROC) curves. We then illustrate their use on real-life medical diagnostic tasks. 1 Introduction. Since their introduction by Vapnik and coworkers [ Vapnik, 1995; Cortes and Vapnik, 1995 ] , Support Vector Machines (SVMs) have been successfully applied to a number of real world problems such as handwritten character and digit recognition [ Scholkopf, 1997; Cortes, 1995; LeCun et al., 1995; Vapnik, 1995 ] , face detection [ Osuna et al., 1997 ] and speaker identification [ Schmidt, 1996 ] . They exhibit a r...