Incremental Hypersphere Classifier (IHC)

Abstract

In the previous chapters we have presented batch learning algorithms, which are designed under the assumptions that data is static and its volume is small (and manageable). Faced with a myriad of high-throughput data usually presenting uncertainty, high dimensionality and large complexity, the batch methods are no longer useful. Using a different approach, incremental algorithms are designed to rapidly update their models to incorporate new information on a sampleby- sample basis. In this chapter we present a novel incremental instance-based learning algorithm, which presents good properties in terms of multi-class support, complexity, scalability and interpretability. The Incremental Hypersphere Classifier (IHC) is tested in well-known benchmarks yielding good classification performance results. Additionally, it can be used as an instance selection method since it preserves class boundary samples.