An experimental approach for prediction of multi-classification using SVM

Abstract

The multiclass classification problem is an important topic in the field of pattern recognition. It involves the task of classifying input instances into one of multiple classes. Since the class overlapping problem exists among multiple classes in most real-world problems, the multiclass classification task is much more complicated and challenging compared to the binary class problem. Classification involves the learning of the mapping function that associates input samples to corresponding target label. There are two major categories of classification problems: Single-label classification and multi-label classification. Traditional binary and multi-class classifications are subcategories of single-label classification. The performance of the developed classifier is evaluated using datasets from binary, multi-class and multi-label problems. The results obtained are compared with state-of-the-art techniques from each of the classification types.