A novel approach for noisy signal classification through the use of multiple wavelets and ensembles of classifiers

Research output: Book chapter/Published conference paperConference paperpeer-review

Abstract

Classification of time series signals can be crucial for many practical applications. While the existing classifiers may accurately classify pure signals, the existence of noise can significantly disturb the classification accuracy of these classifiers. We propose a novel classification approach that uses multiple wavelets together with an ensemble of classifiers to return high classification accuracy even for noisy signals.
The proposed technique has two main steps. In Step 1, We convert raw signals into a useful dataset by applying multiple wavelet transforms, each from a different wavelet family or all from the same family with differing filter lengths. In Step 2, We apply the dataset processed in Step 1 to an ensemble of classifiers. We test on 500 noisy signals from five different classes. Our experimental results demonstrate the effectiveness of the proposed technique, on noisy signals, compared to the approaches that use either raw signals or a single wavelet transform.
Original languageEnglish
Title of host publication15th International Conference on Advanced Data Mining and Applications (ADMA 2019)
EditorsRandy Goebel, Yuzuru Tanaka, Wolfgang Wahlster
Place of PublicationSwitzerland
PublisherSpringer
Pages195-203
Number of pages8
Volume11888
ISBN (Electronic)9783030352318
ISBN (Print)9783030352301
DOIs
Publication statusPublished - Nov 2019
Event15th International Conference on Advanced Data Mining and Applications 2019: ADMA 2019 - Hi Chance (Dalian) Science & Technology Center, Dalian, China
Duration: 21 Nov 201923 Nov 2019
http://adma2019.neusoft.edu.cn/
https://link.springer.com/book/10.1007/978-3-030-35231-8 (proceedings)
http://adma2019.neusoft.edu.cn/wp-content/uploads/2019/11/ADMA-2019-Program_V5.02.pdf (program)

Conference

Conference15th International Conference on Advanced Data Mining and Applications 2019
Country/TerritoryChina
CityDalian
Period21/11/1923/11/19
Internet address

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