SLAYER: A Semi-supervised Learning Approach for Drifting Data Streams under Extreme
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2021
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Classification models learned from data streams often assume the availability of true labels after predicting new examples, either instantly or with some delay with respect to inference time. However, in many real-world scenarios comprising sensors, actuators and robotic swarms, this assumption may not realistically hold, since the supervision of newly classified samples can be unfeasible to achieve in practice. The extreme case where such a supervision is never available is referred to as extreme verification latency. Furthermore, streaming data is also known to undergo the effects of exogenous non-stationary phenomena, by which patterns to be learned from the streams can evolve over time, thereby requiring the adaptation of the classifier for its knowledge to match to the prevailing concept. When these two circumstances (extreme verification latency and concept drift) concur in a given scenario, adapting the model to the evolving dynamics of stream data becomes a challenging task, as the lack of supervision requires rethinking this functionality from a semi-supervised perspective. In this context we present SLAYER, a semi-supervised learning approach capable of tracking the evolution of concepts in the feature space, and analyzing their characteristics towards alleviating the effects of concept drift in the classification accuracy. Besides its continuous adaptation to evolving concepts, another advantage of SLAYER is its resilience against the appearance and disappearance of concepts over time, adapting its knowledge seamlessly when it occurs. We assess the performance of SLAYER over several datasets and compare it to that of state-of-the-art approaches proposed to deal with this stream learning setup. The discussion on the obtained results is conclusive: SLAYER offers a competitive behavior, performing best over several of the datasets considered in the benchmark.
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Arostegi , M , Lobo , J L & Del Ser , J 2021 , ' SLAYER : A Semi-supervised Learning Approach for Drifting Data Streams under Extreme ' , CEUR Workshop Proceedings , vol. 3079 , pp. 50-64 .