Altun H.Polat G.2019-08-012019-08-0120089.78142E+12https://dx.doi.org/10.1109/SIU.2008.4632592https://hdl.handle.net/11480/11172008 IEEE 16th Signal Processing, Communication and Applications Conference, SIU -- 20 April 2008 through 22 April 2008 -- Aydin -- 74111In literature there is a huge body of references available which compare various classifiers in a particular application. However, the reliability of such a comparison is only valid if the model parameters, performance criteria and training environment are chosen in a fair framework, as successful application of a classifier is dependent on the those parameters. In this study we attempt to answer the questions below in a emotion detection framework, using classifiers such as KNN, SVM, RBF and MLP: Is the success of a classifier enough to make the claim that a classifier is "the best one" in a particular classification task? How is it possible to carry out a fair comparison between classifiers? ©2008 IEEE.trinfo:eu-repo/semantics/closedAccessConference Object10.1109/SIU.2008.46325922-s2.0-56449118601N/A