Incremental construction of classifier and discriminant ensembles

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Tarih

2009-04-15

Dergi Başlığı

Dergi ISSN

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Yayıncı

Elsevier Science Inc

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Araştırma projeleri

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Özet

We discuss approaches to incrementally construct an ensemble. The first constructs an ensemble of classifiers choosing a subset from a larger set, and the second constructs an ensemble of discriminants, where a classifier is used for some classes only. We investigate criteria including accuracy, significant improvement, diversity, correlation, and the role of search direction. For discriminant ensembles, we test subset selection and trees. Fusion is by voting or by a linear model. Using 14 classifiers on 38 data sets. incremental search finds small, accurate ensembles in polynomial time. The discriminant ensemble uses a subset of discriminants and is simpler, interpretable, and accurate. We see that an incremental ensemble has higher accuracy than bagging and random subspace method; and it has a comparable accuracy to AdaBoost. but fewer classifiers.

Açıklama

We would like to thank the three anonymous referees and the editor for their constructive comments, pointers to related literature, and pertinent questions which allowed us to better situate our work as well as organize the ms and improve the presentation. This work has been supported by the Turkish Academy of Sciences in the framework of the Young Scientist Award Program (EA-TUBA-GEBIP/2001-1-1), Bogazici University Scientific Research Project 05HA101 and Turkish Scientific Technical Research Council TUBITAK EEEAG 104EO79

Anahtar Kelimeler

Classification, Classifier fusion, Classifier ensembles, Stacking, Machine learning, Voting, Discriminant ensembles, Diversity, Classifiers, Polynomial approximation, Robot learning, Learning systems

Kaynak

Information Sciences

WoS Q Değeri

Q1
Q1

Scopus Q Değeri

Q1

Cilt

179

Sayı

9

Künye

Ulaş, A., Semerci, M., Yıldız, O. T. & Alpaydın, E. (2009). Incremental construction of classifier and discriminant ensembles. Information Sciences, 179(9), 1298-1318. doi:10.1016/j.ins.2008.12.024