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Authors: Xia Jianan ; Sun Dongyi and Xiao Fan

Affiliation: Beijing Jiaotong University, China

Keyword(s): Feature selection, Variable selection, Pattern recognition, LASSO, Ridge regression.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Bioinformatics ; Biomedical Engineering ; Enterprise Information Systems ; Information Systems Analysis and Specification ; Methodologies and Technologies ; Operational Research ; Problem Solving ; Requirements Analysis And Management ; Simulation

Abstract: Feature Selection is one of the focuses in pattern recognition field. To select the most obvious features, there are some feature selection methods such as LASSO, Bridge Regression and so on. But all of them are limited in select feature. In this paper, a summary is listed. And also the advantages and limitations of every method are listed. By the end, an example of LASSO using in identification of Traditional Chinese Medicine is introduced to show how to use these methods to select the feature.

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Paper citation in several formats:
Jianan, X.; Dongyi, S. and Fan, X. (2011). SUMMARY OF LASSO AND RELATIVE METHODS. In Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-8425-54-6; ISSN 2184-4992, SciTePress, pages 131-134. DOI: 10.5220/0003426901310134

@conference{iceis11,
author={Xia Jianan. and Sun Dongyi. and Xiao Fan.},
title={SUMMARY OF LASSO AND RELATIVE METHODS},
booktitle={Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2011},
pages={131-134},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003426901310134},
isbn={978-989-8425-54-6},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - SUMMARY OF LASSO AND RELATIVE METHODS
SN - 978-989-8425-54-6
IS - 2184-4992
AU - Jianan, X.
AU - Dongyi, S.
AU - Fan, X.
PY - 2011
SP - 131
EP - 134
DO - 10.5220/0003426901310134
PB - SciTePress