Akademik Veri Yönetim Sistemi
2nd International Symposium on Applied Sciences and Engineering (ISASE2021), Erzurum, Türkiye, 7 - 09 Nisan 2021, ss.285-289
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.