TY - JOUR
T1 - Discrete neighborhood representations and modified stacked generalization methods for distributed regression
AU - Allende-Cid, Héctor
AU - Allende, Héctor
AU - Monge, Rául
AU - Moraga, Claudio
N1 - Publisher Copyright:
© J.UCS
PY - 2015/7/25
Y1 - 2015/7/25
N2 - 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.
AB - 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.
KW - Context-aware regression
KW - Distributed machine learning
KW - Similarity representation
UR - http://www.scopus.com/inward/record.url?scp=84937821640&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:84937821640
VL - 21
SP - 842
EP - 855
JO - Journal of Universal Computer Science
JF - Journal of Universal Computer Science
SN - 0948-695X
IS - 6
ER -