Cataloged from PDF version of article.For a long time, classification algorithms have focused on minimizing the quantity of
prediction errors by assuming that each possible error has identical consequences.
However, in many real-world situations, this assumption is not convenient. For instance,
in a medical diagnosis domain, misdiagnosing a sick patient as healthy is much more
serious than its opposite. For this reason, there is a great need for new classification
methods that can handle asymmetric cost and benefit constraints of classifications. In this
thesis, we discuss cost-sensitive classification concepts and propose a new classification
algorithm called Benefit Maximization with Feature Intervals (BMFI) that uses the
feature projection based knowledge representation. In the framework of BMFI, we
introduce five different voting methods that are shown to be effective over different
domains. A number of generalization and pruning methodologies based on benefits of
classification are implemented and experimented. Empirical evaluation of the methods
has shown that BMFI exhibits promising performance results compared to recent wrapper
cost-sensitive algorithms, despite the fact that classifier performance is highly dependent
on the benefit constraints and class distributions in the domain. In order to evaluate costsensitive
classification techniques, we describe a new metric, namely benefit accuracy
which computes the relative accuracy of the total benefit obtained with respect to the
maximum possible benefit achievable in the domain.İkizler, NazlıM.S