PaZoe: classifying time series with few labels

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@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",
• 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.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.23919/EUSIPCO54536.2021.9615924",
• ISSN = "2076-1465",
• month = aug,
• notes = "Also known as \cite{9615924}",

}

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

Citations