Abstract
Machine learning techniques are indispensable in today’s data-driven fault diagnosis methodolgoies. Among many machine techniques, k-nearest neighbor (k-NN) is one of the most widely used for fault diagnosis due to its simplicity, effectiveness, and computational efficiency. However, the lack of a density-based affinity measure in the conventional k-NN algorithm can decrease the classification accuracy. To address this issue, a sequential k-NN classification methodology using distance- and density-based affinity measures in a sequential manner is introduced for classification.
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This research was supported by the over 100 CALCE members of the CALCE Consortium and also by the National Natural Science Foundation of China (NSFC) under grant number 71420107023.
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Kang, M., Ramaswami, G.K., Hodkiewicz, M., Cripps, E., Kim, JM., Pecht, M. (2016). A Sequential k-Nearest Neighbor Classification Approach for Data-Driven Fault Diagnosis Using Distance- and Density-Based Affinity Measures. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2016. Lecture Notes in Computer Science(), vol 9714. Springer, Cham. https://doi.org/10.1007/978-3-319-40973-3_25
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DOI: https://doi.org/10.1007/978-3-319-40973-3_25
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