An Incremental Network with Local Experts Ensemble

Ensemble learning algorithms aim to train a group of classifiers to enhance the generalization ability. However, vast of those algorithms are learning in batches and the base classifiers (e.g. number, type) must be predetermined. In this paper, we propose an ensemble algorithm called INLEX (Incremental Network with Local EXperts ensemble) to learn suitable number of linear classifiers in an online incremental mode. Specifically, it incrementally learns the representational nodes of the input space. In the incremental process, INLEX finds nodes in the decision boundary area (boundary nodes) based on the theory of entropy: boundary nodes are considered to be disordered. In this paper, boundary nodes are activated as experts, each of which is a local linear classifier. Combination of these linear experts with dynamical weights will constitute a decision boundary to solve nonlinear classification tasks. Experimental results show that INLEX obtains promising performance on real-world classification benchmarks.