Novel Feature Selection Methods for High Dimensional Data

Verónica Bolón-Canedo, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos

Abstract

Over the last few years, the dimensionality of datasets involved in data mining applications has increased dramatically. In this situation, feature selection becomes indispensable as it allows for dimensionality reduction and relevance detection. This paper is devoted to study the impact of feature selection on high-dimensonal data as well as to present novel methods. After demonstrating the adequacy of feature selection on real applications, new methods are described which cover different topics such as ensemble learning, distributed learning, scalability of algorithms or cost-based feature selection.

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Paper Citation


in Harvard Style

Bolón-Canedo V., Sánchez-Maroño N. and Alonso-Betanzos A. (2014). Novel Feature Selection Methods for High Dimensional Data . In Doctoral Consortium - DCAART, (ICAART 2014) ISBN Not Available, pages 3-14


in Bibtex Style

@conference{dcaart14,
author={Verónica Bolón-Canedo and Noelia Sánchez-Maroño and Amparo Alonso-Betanzos},
title={Novel Feature Selection Methods for High Dimensional Data},
booktitle={Doctoral Consortium - DCAART, (ICAART 2014)},
year={2014},
pages={3-14},
publisher={SciTePress},
organization={INSTICC},
doi={},
isbn={Not Available},
}


in EndNote Style

TY - CONF
JO - Doctoral Consortium - DCAART, (ICAART 2014)
TI - Novel Feature Selection Methods for High Dimensional Data
SN - Not Available
AU - Bolón-Canedo V.
AU - Sánchez-Maroño N.
AU - Alonso-Betanzos A.
PY - 2014
SP - 3
EP - 14
DO -