Small and Unbalanced Data Set Problem in Classification

Abstract

Classification of data is difficult in case of small and unbalanced data set and this problem directly affects the classification performance. Small and / or the imbalance dataset has become a major problem in data mining. Classification algorithms are developed based on the assumption that the data sets are balanced and large enough. The most of the algorithms ignore or misclassify examples of the minority class, focus on the majority class. Small and unbalanced data set problem is frequently encountered in medical data mining due to some limitations. Within the scope of the study, the public accessible data set, hepatitis, was divided into small and imblanced data subsets, each of the data subsets were oversampled by distance based data generation methods. The oversampled data sets were classified by using four different machine learning algorithms (Artificial Neural Networks, Support Vector Machines, Naive Bayes and Decision Tree) and the classification scores were compared.