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Subject Item
n2:79330
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bibo:Article bibo:AcademicArticle n7:EPrint n7:ArticleEPrint
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n13:j.tcs.2019.12.002
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n8:3196830
dcterms:title
An evolutionary algorithm for automated machine learning focusing on classifier ensembles: an improved algorithm and extended results
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dcterms:date
2020-01-18
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n4:ext-a.a.freitas@kent.ac.uk n4:ext-1ea1b4cd2c6b4e763f7b4d1bd2fe2f5a n4:ext-bb28e84feed864f9f438674efa72a67a n4:ext-22ab319f6f23af41215e92bbb3ba45fa n4:ext-0347d50ddc2574fc3e49574b937fdd09
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n17:ext-f308aad1ef8f70546c3a197f104f2ad5
bibo:abstract
A large number of classification algorithms have been proposed in the machine learning literature. These algorithms have different pros and cons, and no algorithm is the best for all datasets. Hence, a challenging problem consists of choosing the best classification algorithm with its best hyper-parameter settings for a given input dataset. In the last few years, Automated Machine Learning (Auto-ML) has emerged as a promising approach for tackling this problem, by doing a heuristic search in a large space of candidate classification algorithms and their hyper-parameter settings. In this work we propose an improved version of our previous Evolutionary Algorithm (EA) – more precisely, an Estimation of Distribution Algorithm – for the Auto-ML task of automatically selecting the best classifier ensemble and its best hyper-parameter settings for an input dataset. The new version of this EA was compared against its previous version, as well as against a random forest algorithm (a strong ensemble algorithm) and a version of the well-known Auto-ML method Auto-WEKA adapted to search in the same space of classifier ensembles as the proposed EA. In general, in experiments with 21 datasets, the new EA version obtained the best results among all methods in terms of four popular predictive accuracy measures: error rate, precision, recall and F-measure.
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n16:ext-03043975 n21:repository
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n6:Q335
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n15:authors
bibo:volume
805