Discrete neighborhood representations and modified stacked generalization methods for distributed regression

When distributed data sources have different contexts the problem of Distributed Regression becomes severe. It is the underlying law of probability that constitutes the context of a source. A new Distributed Regression System is presented,which makes use of a discrete representation of the probability density functions (pdfs). Neighborhoods of similar datasets are detected by comparing their approximated pdfs. This information supports an ensemble-based approach,and the improvement of a second level unit,as it is the case in stacked generalization. Two synthetic and six real data sets are used to compare the proposed method with other state-of-the-art models. The obtained results are positive for most datasets. © J.UCS