PUBLICATION DATE

2017-01-23

AUTHORS

Diego P. P. Mesquita, João P. P. Gomes, Amauri H. Souza Junior

TITLE

Ensemble of Efficient Minimal Learning Machines for Classification and Regression

ISSUE

N/A

VOLUME

N/A

ISSN (print)

1370-4621

ISSN (electronic)

1573-773X

ABSTRACT

Minimal Learning Machine (MLM) is a recently proposed supervised learning algorithm with performance comparable to most state-of-the-art machine learning methods. In this work, we propose ensemble methods for classification and regression using MLMs. The goal of ensemble strategies is to produce more robust and accurate models when compared to a single classifier or regression model. Despite its successful application, MLM employs a computationally intensive optimization problem as part of its test procedure (out-of-sample data estimation). This becomes even more noticeable in the context of ensemble learning, where multiple models are used. Aiming to provide fast alternatives to the standard MLM, we also propose the Nearest Neighbor Minimal Learning Machine and the Cubic Equation Minimal Learning Machine to cope with classification and single-output regression problems, respectively. The experimental assessment conducted on real-world datasets reports that ensemble of fast MLMs perform comparably or superiorly to reference machine learning algorithms.

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32 TRIPLES      26 PREDICATES      33 URIs      17 LITERALS

Subject Predicate Object
1 articles:b241e99d2399ab01ce8bbd6bdb380a3f sg:abstract Abstract Minimal Learning Machine (MLM) is a recently proposed supervised learning algorithm with performance comparable to most state-of-the-art machine learning methods. In this work, we propose ensemble methods for classification and regression using MLMs. The goal of ensemble strategies is to produce more robust and accurate models when compared to a single classifier or regression model. Despite its successful application, MLM employs a computationally intensive optimization problem as part of its test procedure (out-of-sample data estimation). This becomes even more noticeable in the context of ensemble learning, where multiple models are used. Aiming to provide fast alternatives to the standard MLM, we also propose the Nearest Neighbor Minimal Learning Machine and the Cubic Equation Minimal Learning Machine to cope with classification and single-output regression problems, respectively. The experimental assessment conducted on real-world datasets reports that ensemble of fast MLMs perform comparably or superiorly to reference machine learning algorithms.
2 sg:articleType OriginalPaper
3 sg:ddsId s11063-017-9587-5
4 sg:ddsIdJournalBrand 11063
5 sg:doi 10.1007/s11063-017-9587-5
6 sg:doiLink http://dx.doi.org/10.1007/s11063-017-9587-5
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19 sg:issnElectronic 1573-773X
20 sg:issnPrint 1370-4621
21 sg:language English
22 sg:license http://scigraph.springernature.com/explorer/license/
23 sg:pageEnd 16
24 sg:pageStart 1
25 sg:publicationDate 2017-01-23
26 sg:publicationYear 2017
27 sg:publicationYearMonth 2017-01
28 sg:scigraphId b241e99d2399ab01ce8bbd6bdb380a3f
29 sg:title Ensemble of Efficient Minimal Learning Machines for Classification and Regression
30 sg:webpage https://link.springer.com/10.1007/s11063-017-9587-5
31 rdf:type sg:Article
32 rdfs:label Article: Ensemble of Efficient Minimal Learning Machines for Classification and Regression
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