  In this paper we present an incremental variant of the Twin Support Vector
Machine (TWSVM) called Fuzzy Bounded Twin Support Vector Machine (FBTWSVM) to
deal with large datasets and learning from data streams. We combine the TWSVM
with a fuzzy membership function, so that each input has a different
contribution to each hyperplane in a binary classifier. To solve the pair of
quadratic programming problems (QPPs) we use a dual coordinate descent
algorithm with a shrinking strategy, and to obtain a robust classification with
a fast training we propose the use of a Fourier Gaussian approximation function
with our linear FBTWSVM. Inspired by the shrinking technique, the incremental
algorithm re-utilizes part of the training method with some heuristics, while
the decremental procedure is based on a scored window. The FBTWSVM is also
extended for multi-class problems by combining binary classifiers using a
Directed Acyclic Graph (DAG) approach. Moreover, we analyzed the theoretical
foundations properties of the proposed approach and its extension, and the
experimental results on benchmark datasets indicate that the FBTWSVM has a fast
training and retraining process while maintaining a robust classification
performance.
