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Authors: Verónica Bolón-Canedo ; Diego Peteiro-Barral ; Amparo Alonso-Betanzos ; Bertha Guijarro-Berdiñas and Noelia Sánchez-Maroño

Affiliation: University of A Coruña, Spain

ISBN: 978-989-758-015-4

Keyword(s): Feature Selection, Classification, Distributed Learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Symbolic Systems

Abstract: In the last few years, distributed learning has been the focus of much attention due to the explosion of big databases, in some cases distributed across different nodes. However, the great majority of current selection and classification algorithms are designed for centralized learning, i.e. they use the whole dataset at once. In this paper, a new approach for learning on vertically partitioned data is presented, which covers both feature selection and classification. The approach splits the data by features, and then uses the c2 filter and the naive Bayes classifier to learn at each node. Finally, a merging procedure is performed, which updates the learned model in an incremental fashion. The experimental results on five representative datasets show that the execution time is shortened considerably whereas the classification performance is maintained as the number of nodes increases.

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Paper citation in several formats:
Bolón-Canedo, V.; Peteiro-Barral, D.; Alonso-Betanzos, A.; Guijarro-Berdiñas, B. and Sánchez-Maroño, N. (2014). Learning on Vertically Partitioned Data based on Chi-square Feature Selection and Naive Bayes Classification.In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4, pages 350-357. DOI: 10.5220/0004759503500357

@conference{icaart14,
author={Verónica Bolón{-}Canedo. and Diego Peteiro{-}Barral. and Amparo Alonso{-}Betanzos. and Bertha Guijarro{-}Berdiñas. and Noelia Sánchez{-}Maroño.},
title={Learning on Vertically Partitioned Data based on Chi-square Feature Selection and Naive Bayes Classification},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2014},
pages={350-357},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004759503500357},
isbn={978-989-758-015-4},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Learning on Vertically Partitioned Data based on Chi-square Feature Selection and Naive Bayes Classification
SN - 978-989-758-015-4
AU - Bolón-Canedo, V.
AU - Peteiro-Barral, D.
AU - Alonso-Betanzos, A.
AU - Guijarro-Berdiñas, B.
AU - Sánchez-Maroño, N.
PY - 2014
SP - 350
EP - 357
DO - 10.5220/0004759503500357

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