An Ensemble-based Dimensionality Reduction for Service Monitoring Time-series

Farzana Anowar, Farzana Anowar, Samira Sadaoui, Hardik Dalal

2022

Abstract

Our work introduces an ensemble-based dimensionality reduction approach to efficiently address the high dimensionality of an industrial unlabeled time-series dataset, intending to produce robust data labels. The ensemble comprises a self-supervised learning method to improve data quality, an unsupervised dimensionality reduction to lower the ample feature space, and a chunk-based incremental dimensionality reduction to further increase confidence in data labels. Since the time-series dataset is massive, we divide it into several chunks and evaluate each chunk’s quality using time-series clustering method and metrics. The experiments reveal that clustering performances increased significantly for all the chunks after performing the ensemble approach.

Paper Citation

in Harvard Style

Anowar F., Sadaoui S. and Dalal H. (2022). An Ensemble-based Dimensionality Reduction for Service Monitoring Time-series. In Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA, ISBN 978-989-758-584-5, pages 117-124. DOI: 10.5220/0011273700003277

in Bibtex Style

@conference{delta22,
author={Farzana Anowar and Samira Sadaoui and Hardik Dalal},
title={An Ensemble-based Dimensionality Reduction for Service Monitoring Time-series},
booktitle={Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,},
year={2022},
pages={117-124},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011273700003277},
isbn={978-989-758-584-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,
TI - An Ensemble-based Dimensionality Reduction for Service Monitoring Time-series
SN - 978-989-758-584-5
AU - Anowar F.
AU - Sadaoui S.
AU - Dalal H.
PY - 2022
SP - 117
EP - 124
DO - 10.5220/0011273700003277