Fixed-Size Least Squares Support Vector Machines: Scala Implementation for Large Scale Classification

We propose FS-Scala, a flexible and modular Scala based implementation of the Fixed Size Least Squares Support Vector Machine (FS-LSSVM) for large data sets. The framework consists of a set of modules for (gradient and gradient free) optimization, model representation, kernel functions and evaluation of FS-LSSVM models. A kernel based Fixed-Size Least Squares Support Vector Machine (FS-LSSVM) model is implemented in the proposed framework, while heavily leveraging the parallel computing capabilities of Apache Spark. Global optimization routines like Coupled Simulated Annealing (CSA) and Grid Search are implemented and used to tune the hyper-parameters of the FS-LSSVM model. Finally, we carry out experiments on benchmark data sets and evaluate the performance of various kernel based FS-LSSVM models.