Hyperparameter self-tuning for data streams

Híbrido- Sala ec 2.10 - Edifício 12 - Escola de… | | 14:30

 Bruno Miguel Veloso

Universidade Portucalense e  Laboratório de Inteligência Artificial e Apoio à Decisão, do INESC TEC



The number of Internet of Things devices generating data streams is expected to grow exponentially with the support of emergent technologies such as 5G networks. Therefore, the online processing of these data streams requires the design and development of suitable machine learning algorithms, able to learn online, as data is generated. Like their batch-learning counterparts, stream-based learning algorithms require careful hyper parameter settings. However, this problem is exacerbated in online learning settings, especially with the occurrence of concept drifts, which frequently require the reconfiguration of hyper parameters. We present the Self Parameter Tuning (SPT) optimisation algorithm for data streams. We apply our algorithm to dynamically-sized samples, converging to optimal settings in a single pass over data while using a relatively small number of hyper parameter configurations. In addition, our proposal automatically readjusts hyper parameters when concept drift occurs.

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