Comparison of Fast Learning Large-Scale Multi-Class Classification.
Aleksander Jovanovich, Alina Lazar · MAICS · 2013
Recent progress in the development of techniques to optimize large-scale classification problems has extended the use of multi-class classification. Specifically the use of multi-class classification algorithms when the dataset is to large to fit into limited memory available of most computers. The most prominent algorithms used today solve the multi-class classification problem through an optimization approach based on coordinate decent. Two of the most recognized algorithms, Vowpal Wabbit and LIBLINEAR LibSVM have emerged as the most consistent options when solving for a multi-class problems. This paper proposes an analysis of these methods and tests the efficiency and performance of each algorithm. The results are recorded and comparisons are made. After analyzing the results, the conclusion made is that the Vowpal Wabbit algorithm is best suited for solving large-scale multi-class classification problems when computer memory is constrained.