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.
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 language | English |
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| Title of host publication | 15th International Conference on Advanced Data Mining and Applications (ADMA 2019) |
| Editors | Randy Goebel, Yuzuru Tanaka, Wolfgang Wahlster |
| Place of Publication | Switzerland |
| Publisher | Springer |
| Pages | 195-203 |
| Number of pages | 8 |
| Volume | 11888 |
| ISBN (Electronic) | 9783030352318 |
| ISBN (Print) | 9783030352301 |
| DOIs | |
| Publication status | Published - Nov 2019 |
| Event | 15th International Conference on Advanced Data Mining and Applications 2019: ADMA 2019 - Hi Chance (Dalian) Science & Technology Center, Dalian, China Duration: 21 Nov 2019 → 23 Nov 2019 https://web.archive.org/web/20191216185322/http://adma2019.neusoft.edu.cn/ (Conference website) https://link.springer.com/book/10.1007/978-3-030-35231-8 (proceedings) |
Conference
| Conference | 15th International Conference on Advanced Data Mining and Applications 2019 |
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| Country/Territory | China |
| City | Dalian |
| Period | 21/11/19 → 23/11/19 |
| Other | The conference aims at bringing together the experts on data mining from around the world, and providing a leading international forum for the dissemination of original research findings in data mining, spanning applications, algorithms, software and systems, as well as different applied disciplines with potential in data mining, such as smartphone and social network mining, bio-medical science and green computing. ADMA 2019 will promote the same close interaction and collaboration among practitioners and researchers. Published papers will go through a full peer review process. |
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