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Authors: Kengo Ooi and Takashi Ninomiya

Affiliation: Ehime University, Japan

Keyword(s): Machine Learning, Feature Selection, Grafting, ℓ1-Regularized Logistic Regression.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems

Abstract: Finding features for classifiers is one of the most important concerns in various fields, such as information retrieval, speech recognition, bio-informatics and natural language processing, for improving classifier prediction performance. Online grafting is one solution for finding useful features from an extremely large feature set. Given a sequence of features, online grafting selects or discards each feature in the sequence of features one at a time. Online grafting is preferable in that it incrementally selects features, and it is defined as an optimization problem based on ℓ1-regularized logistic regression. However, its learning is inefficient due to frequent parameter optimization. We propose two improved methods, in terms of efficiency, for online grafting that approximate original online grafting by testing multiple features simultaneously. The experiments have shown that our methods significantly improved efficiency of online grafting. Though our methods are approximation t echniques, deterioration of prediction performance was negligibly small. (More)

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Paper citation in several formats:
Ooi, K. and Ninomiya, T. (2013). Efficient Online Feature Selection based on ℓ1-Regularized Logistic Regression. In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-8565-39-6; ISSN 2184-433X, SciTePress, pages 277-282. DOI: 10.5220/0004255902770282

@conference{icaart13,
author={Kengo Ooi. and Takashi Ninomiya.},
title={Efficient Online Feature Selection based on ℓ1-Regularized Logistic Regression},
booktitle={Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2013},
pages={277-282},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004255902770282},
isbn={978-989-8565-39-6},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Efficient Online Feature Selection based on ℓ1-Regularized Logistic Regression
SN - 978-989-8565-39-6
IS - 2184-433X
AU - Ooi, K.
AU - Ninomiya, T.
PY - 2013
SP - 277
EP - 282
DO - 10.5220/0004255902770282
PB - SciTePress