Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 34, Issue 9, Pages 6029-6041Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2021.3132584
Keywords
Training; Manifolds; Labeling; Neural networks; Semisupervised learning; Clustering algorithms; Software; Data stream learning; manifold learning; neural networks; nonstationary environments; online semisupervised learning
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Learning from data streams in nonstationary environments is important and challenging. Existing approaches rely on labeled data to handle concept drifts, which is expensive. We propose a novel algorithm that uses unlabeled data to tackle concept drifts and learns meaningful data representations with an online semisupervised neural network.
Learning from data streams that emerge from nonstationary environments has many real-world applications and poses various challenges. A key characteristic of such a task is the varying nature of target functions and data distributions over time (concept drifts). Most existing work relies solely on labeled data to adapt to concept drifts in classification problems. However, labeling all instances in a potentially life-long data stream is frequently prohibitively expensive, hindering such approaches. Therefore, we propose a novel algorithm to exploit unlabeled instances, which are typically plentiful and easily obtained. The algorithm is an online semisupervised radial basis function neural network (OSNN) with manifold-based training to exploit unlabeled data while tackling concept drifts in classification problems. OSNN employs a novel semisupervised learning vector quantization (SLVQ) to train network centers and learn meaningful data representations that change over time. It uses manifold learning on dynamic graphs to adjust the network weights. Our experiments confirm that OSNN can effectively use unlabeled data to elucidate underlying structures of data streams while its dynamic topology learning provides robustness to concept drifts.
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