Comparison of Classifiers According to Number of Trials

Pattern recognition and machine learning

based studies have attracted considerable interest in recent

years. Because of the fact that there is no a fixed feature and

classification method that can solve all kinds of pattern

recognition and machine learning problem, in studies

determining the most appropriate feature and classifier

together with its parameters requires research and is a timeconsuming process. Comparison of classifiers from different

perspectives gives the researchers an idea about the solution

of their problems. In the literature, while classifiers were

compared in different feature distributions, sizes or

classification performance criteria, there were limited studies

about classifiers which compared in terms of number of trials

which was an important parameter especially for researchers.

In this study, Support Vector Machines, k Nearest Neighbor,

and Linear Discrimination Analysis classifiers for four

different feature spaces were compared in terms of different

trial counts. The results revealed that the classification

accuracy for different distribution types and classifiers

changed depending on the number of trials. Furthermore, as

the number of trials increased, the standard deviation

decreased. It means that the stability increased as expected

with the increase in the number of trials. It can be said that

the k Nearest Neighbor algorithm generally provides superior

classification accuracy for all types of distributions.