dc.contributor.advisor | Can, Fazlı | |
dc.contributor.author | Elbaşı, Sanem | |
dc.date.accessioned | 2019-09-30T11:36:14Z | |
dc.date.available | 2019-09-30T11:36:14Z | |
dc.date.copyright | 2019-09 | |
dc.date.issued | 2019-09 | |
dc.date.submitted | 2019-09-27 | |
dc.identifier.uri | http://hdl.handle.net/11693/52517 | |
dc.description | Cataloged from PDF version of article. | en_US |
dc.description | Thesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2019. | en_US |
dc.description | Includes bibliographical references (leaves 40-45). | en_US |
dc.description.abstract | Ensemble pruning is the process of selecting a subset of component classifiers
from an ensemble which performs at least as well as the original ensemble while
reducing storage and computational costs. Ensemble pruning in data streams is a
largely unexplored area of research. It requires analysis of ensemble components
as they are running on the stream and differentiation of useful classifiers from redundant
ones. We present two on-the-fly ensemble pruning methods; Class-wise
Component Ranking-based Pruner (CCRP) and Cover Coefficient-based Pruner
(CCP). CCRP aims that the resulting pruned ensemble contains the best performing
classifier for each target class and hence, reduces the effects of class imbalance.
On the other hand, CCP aims to select components that make misclassification
errors on different instances. The conducted experiments on real-world and synthetic
data streams demonstrate that different types of ensembles that integrate
pruners consume significantly less memory and perform significantly faster without
hurting the predictive performance. | en_US |
dc.description.statementofresponsibility | by Sanem Elbaşı | en_US |
dc.format.extent | xi, 48 leaves : charts (some color) ; 30 cm. | en_US |
dc.language.iso | English | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Ensemble learning | en_US |
dc.subject | Ensemble pruning | en_US |
dc.subject | Ensemble efficiency | en_US |
dc.subject | Concept drift | en_US |
dc.title | On-the-fly ensemble classifier pruning in evolving data streams | en_US |
dc.title.alternative | Evrilen veri akışlarında heyet sınıflandırıcıların anında budanması | en_US |
dc.type | Thesis | en_US |
dc.department | Department of Computer Engineering | en_US |
dc.publisher | Bilkent University | en_US |
dc.description.degree | M.S. | en_US |
dc.identifier.itemid | B126693 | |
dc.embargo.release | 2020-03-27 | |