MS THESIS PRESENTATION: GOOWE-ML: A Novel Online Stacked Ensemble for Multi-label Classification in Data Streams<\/p>\n
Alican B\u00fcy\u00fck\u00e7ak\u0131r
\nMS Student
\n(Supervisor: Prof. Dr. Fazl\u0131 Can)<\/p>\n
Computer Engineering Department<\/p>\n
As data streams become more prevalent, the necessity for online algorithms that mine this transient and dynamic data becomes clearer. Multi-label data stream classification is a supervised learning problem where each instance in the data stream is classified into one or more pre-defined sets of labels. Many methods have been proposed to tackle this problem, including but not limited to ensemble-based methods. Some of these ensemble-based methods are specifically designed to work with certain multi-label base classifiers; some others employ online bagging schemes to build their ensembles. In this study, we introduce a novel online and dynamically-weighted stacked ensemble for multi-label classification, called GOOWE-ML, that utilizes spatial modeling to assign optimal weights to its component classifiers. Our model can be used with any existing incremental multi-label classification algorithm as its base classifier. We conduct experiments with 4 GOOWE-ML-based multi-label ensembles and 7 baseline models on 7 real-world datasets from diverse areas of interest. Our experiments show that GOOWE-ML ensembles yield consistently better results in terms of predictive performance in almost all of the datasets, with respect to the other prominent ensemble models.<\/p>\n
DATE: 11 July 2019, Thursday @ 11:00
\nPLACE: EA-409<\/p>\n","protected":false},"excerpt":{"rendered":"
MS THESIS PRESENTATION: GOOWE-ML: A Novel Online Stacked Ensemble for Multi-label Classification in Data Streams Alican B\u00fcy\u00fck\u00e7ak\u0131r MS Student (Supervisor: Prof. Dr. Fazl\u0131 Can) Computer Engineering Department As data streams become more prevalent, the necessity for online algorithms that mine this transient and dynamic data becomes clearer. Multi-label data stream [\u2026] <\/p>\n