AutoML for Stream k-Nearest Neighbors Classification

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Bahri, M (Author) Other; Veloso, B (Author) FEP; Bifet, A (Author) Other; João Gama (Author) FEP

Conference proceedings International

Title: 2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)

Pages: 597-602

8th IEEE International Conference on Big Data (Big Data)

ELECTR NETWORK, DEC 10-13, 2020

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 8 Citations

Other information

Authenticus ID: P-00T-MYW

DOI: 10.1109/bigdata50022.2020.9378396

Abstract (EN): The last few decades have witnessed a significant evolution of technology in different domains, changing the way the world operates, which leads to an overwhelming amount of data generated in an open-ended way as streams. Over the past years, we observed the development of several machine learning algorithms to process big data streams. However, the accuracy of these algorithms is very sensitive to their hyper-parameters, which requires expertise and extensive trials to tune. Another relevant aspect is the high-dimensionality of data, which can causes degradation to computational performance. To cope with these issues, this paper proposes a stream k-nearest neighbors (kNN) algorithm that applies an internal dimension reduction to the stream in order to reduce the resource usage and uses an automatic monitoring system that tunes dynamically the configuration of the kNN algorithm and the output dimension size with big data streams. Experiments over a wide range of datasets show that the predictive and computational performances of the kNN algorithm are improved.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6