Title :
Precision and Recall in Rough Support Vector Machines
Author :
Lingras, Pawan ; Butz, C.J.
Author_Institution :
St. Mary´´s Univ. Halifax, Halifax
Abstract :
Rough support vector machines (RSVMs) supplement conventional support vector machines (SVMs) by providing a better representation of the boundary region. Increasing interest has been paid to the theoretical development of RSVMs, which has already lead to a modification of existing SVM implementations as RSVMs. This paper shows how to extend the use of precision and recall from a SVM implementation to a RSVM implementation. Our approach is demonstrated in practice with the help of Gist, a popular SVM implementation.
Keywords :
rough set theory; support vector machines; RSVM; boundary region representation; rough support vector machines; Computer science; Kernel; Mathematics; Multi-layer neural network; Multilayer perceptrons; Neural networks; Particle measurements; Set theory; Support vector machine classification; Support vector machines;
Conference_Titel :
Granular Computing, 2007. GRC 2007. IEEE International Conference on
Conference_Location :
Fremont, CA
Print_ISBN :
978-0-7695-3032-1
DOI :
10.1109/GrC.2007.77