PaZoe: classifying time series with few labels

Created by W.Langdon from gp-bibliography.bib Revision:1.8939

@InProceedings{Kamalov:2021:EUSIPCO,

• author = "Mikhail Kamalov and Aurelie Boisbunon and Carlo Fanara and Ingrid Grenet and Jonathan Daeden",
• title = "{PaZoe:} classifying time series with few labels",
• booktitle = "2021 29th European Signal Processing Conference (EUSIPCO)",
• year = "2021",
• pages = "1561--1565",
• address = "Dublin",
• publisher = "IEEE",
• month = "23-27 " # aug,
• keywords = "genetic algorithms, genetic programming, Training, Time series analysis, Stacking, Signal processing algorithms, Signal processing, Semisupervised learning, temporal data, semi-supervised classification, PCA, PageRank, symbolic regression",
• ISSN = "2076-1465",
• isbn13 = "978-1-6654-0900-1",
• DOI = "10.23919/EUSIPCO54536.2021.9615924",
• abstract = "Semi-Supervised Learning (SSL) on graph-based datasets is a rapidly growing area of research, but its application to time series is difficult due to the time dimension. We propose a flexible SSL framework based on the stacking of PageRank, PCA and Zoetrope Genetic Programming algorithms into a novel framework: PaZoe. This self-labeling framework shows that graph-based and non-graph based algorithms jointly improve the quality of predictions and outperform each component taken alone. We also show that PaZoe outperforms state-of-the-art SSL algorithms on three time series datasets close to real world conditions. A first set was generated in house, taking data from industrial graded equipment in order to mimick DC motors during operation. Two other datasets, which include the recording of gestures, were taken from the public domain.",
• notes = "Also known as \cite{9615924}",

}

Genetic Programming entries for Mikhail Kamalov Aurelie Boisbunon Carlo Fanara Ingrid Grenet Jonathan Daeden

Citations