Self Hyper-Parameter Tuning for Data Streams

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Veloso, B (Author) Other; João Gama (Author) FEP; Malheiro, B (Author) Other

Conference proceedings International

Title: Discovery Science - 21st International Conference, DS 2018, Limassol, Cyprus, October 29-31, 2018, Proceedings

Pages: 241-255

21st International Conference on Discovery Science, DS 2018

29 October 2018 through 31 October 2018

Indexing

Scopus - 24 Citations

Other information

Authenticus ID: P-00P-RZF

DOI: 10.1007/978-3-030-01771-2_16

Abstract (EN): The widespread usage of smart devices and sensors together with the ubiquity of the Internet access is behind the exponential growth of data streams. Nowadays, there are hundreds of machine learning algorithms able to process high-speed data streams. However, these algorithms rely on human expertise to perform complex processing tasks like hyper-parameter tuning. This paper addresses the problem of data variability modelling in data streams. Specifically, we propose and evaluate a new parameter tuning algorithm called Self Parameter Tuning (SPT). SPT consists of an online adaptation of the Nelder & Mead optimisation algorithm for hyper-parameter tuning. The method explores a dynamic size sample method to evaluate the current solution, and uses the Nelder & Mead operators to update the current set of parameters. The main contribution is the adaptation of the Nelder-Mead algorithm to automatically tune regression hyper-parameters for data streams. Additionally, whenever concept drifts occur in the data stream, it re-initiates the search for new hyper-parameters. The proposed method has been evaluated on regression scenario. Experiments with well known time-evolving data streams show that the proposed SPT hyper-parameter optimisation outperforms the results of previous expert hyper-parameter tuning efforts. © 2018, Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific