Computation of meta-learning classifiers in Distributed Data Mining using a novel cognitive memory model

Abstract

Distributed Data Mining (DDM) performs partial analysis of data at distributed locations and sends a summarized version to the peer sites or a central location for further analysis. Meta-learning is a technique that generates local classifiers (concepts or models) from distributed data sets to use in producing a global classifier. This inherently distributed nature of meta-learning provides much advantage in implementing practical DDM systems. Currently machine learning techniques such as supervised neural networks, decision trees, rules and genetic algorithms are used in the meta-learning process. Inspired by the cognitive representation of human memory, this paper presents a novel mechanis..