A Semi-supervised Based Framework for Data Stream Classification in Non-Stationary Environments

Semi-supervised learning (SSL) is a paradigm that has been continuously used in data classification tasks in datasets that do not have enough labeled instances to train a supervised model with a minimum acceptable accuracy. In this context, data stream classification in dynamic environments appears...

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Bibliographic Details
Main Authors: Gorgonio, Arthur Costa, de P. Canuto, Anne Magaly, Vale, Karliane M. O., Gorgonio, Flavius L.
Format: Conference Proceeding
Language:English
Subjects:
concept drift
Data models
data stream classification
Heuristic algorithms
Labeling
Semi-supervised learning
Semisupervised learning
Supervised learning
Task analysis
Training
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Summary:Semi-supervised learning (SSL) is a paradigm that has been continuously used in data classification tasks in datasets that do not have enough labeled instances to train a supervised model with a minimum acceptable accuracy. In this context, data stream classification in dynamic environments appears as a natural application for this approach, because changes in data distribution contribute to decrease the performance of the classification algorithms. In this paper, we have proposed a framework, refered to as Dynamic Data Stream Learning (DyDaSL), that implements an auto-adaptive classifier ensemble - which is able to evaluate and replace classifiers with decreasing performance. This platform uses the FlexCon-C method, which is a variant of the self-training SSL algorithm that adjusts a confidence threshold dynamically, in each iteration, to define which instances will be labeled. Experimental tests on synthetic and real datasets show that the proposed approach obtains better results than traditional approaches using four evaluation metrics: accuracy, F-score, precision, and recall.
ISSN:2161-4407
DOI:10.1109/IJCNN48605.2020.9206792