GOOWE-ML: a novel online stacked ensemble for multi-label classification in data streams
Author(s)
Advisor
Can, FazlıDate
2019-07Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
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 ensemblebased
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 multilabel
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.