Discrete neighborhood representations and modified stacked generalization methods for distributed regression

Héctor Allende-Cid, Héctor Allende, Rául Monge, Claudio Moraga

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)842-855
Number of pages14
JournalJournal of Universal Computer Science
Volume21
Issue number6
StatePublished - 25 Jul 2015
Externally publishedYes

Keywords

  • Context-aware regression
  • Distributed machine learning
  • Similarity representation

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