Incremental construction of classifier and discriminant ensembles
Yükleniyor...
Tarih
2009-04-15
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Science Inc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
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
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
