Learning on Vertically Partitioned Data based on Chi-square Feature Selection and Naive Bayes Classification

Verónica Bolón-Canedo, Diego Peteiro-Barral, Amparo Alonso-Betanzos, Bertha Guijarro-Berdiñas, Noelia Sánchez-Maroño

2014

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.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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