Classifying Unbalanced Datasets Using Iterative Fuzzy Support Vector Machine

In real world applications, training the classifier using unbalanced dataset is the major problem, as it decreases the performance of Machine Learning algorithms. Unbalanced dataset can be prominently classified based on Support Vector Machine (SVM) which uses Kernel technique to find decision boundary. High Dimensionality and uneven distribution of data has a significant impact on the decision boundary. By employing Feature selection (FS) high dimensionality of data can be solved by selecting prominent features. It is usually applied as a pre-processing step in both soft computing and machine learning tasks. FS is employed in different applications with a variety of purposes: to overcome the curse of dimensionality, to speed up the classification model construction, to help unravel and interpret the innate structure of data sets, to streamline data collection when the measurement cost of attributes are considered and to remove irrelevant and redundant features thus improving classification performance. Hence, in this paper, two different FS approaches has been proposed namely Fuzzy Rough set based FS and Fuzzy Soft set based FS. After FS the reduced dataset has been given to the proposed Iterative Fuzzy Support Vector Machine (IFSVM) for classification which has considered two different membership functions. The Experiments has been carried out on four different data sets namely Thyroid, Breast Cancer, Thoracic surgery, and Heart Disease. The results shown that the classification accuracy is better for Fuzzy Rough set based FS when compared other.