Scalability Analysis of mRMR for Microarray Data

Diego Rego-Fernández, Verónica Bolón-Canedo, Amparo Alonso-Betanzos


Lately, derived from the Big Data problem, researchers in Machine Learning became also interested not only in accuracy, but also in scalability. Although scalability of learning methods is a trending issue, scalability of feature selection methods has not received the same amount of attention. In this research, an attempt to study scalability of both Feature Selection and Machine Learning on microarray datasets will be done. For this sake, the minimum redundancy maximum relevance (mRMR) filter method has been chosen, since it claims to be very adequate for this type of datasets. Three synthetic databases which reflect the problematics of microarray will be evaluated with new measures, based not only in an accurate selection but also in execution time. The results obtained are presented and discussed.


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Paper Citation

in Harvard Style

Rego-Fernández D., Bolón-Canedo V. and Alonso-Betanzos A. (2014). Scalability Analysis of mRMR for Microarray Data . In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4, pages 380-386. DOI: 10.5220/0004807703800386

in Bibtex Style

author={Diego Rego-Fernández and Verónica Bolón-Canedo and Amparo Alonso-Betanzos},
title={Scalability Analysis of mRMR for Microarray Data},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},

in EndNote Style

JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Scalability Analysis of mRMR for Microarray Data
SN - 978-989-758-015-4
AU - Rego-Fernández D.
AU - Bolón-Canedo V.
AU - Alonso-Betanzos A.
PY - 2014
SP - 380
EP - 386
DO - 10.5220/0004807703800386