A Sequential K-Nearest Neighbor Classification Approach For Data-Driven Fault Diagnosis Using Distance- And Density-Based Affinity Measures
DATA MINING AND BIG DATA, DMBD 2016(2016)
摘要
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.
更多查看译文
关键词
Data-driven fault diagnosis, Density-based affinity measure, k-Nearest neighbor, Machine learning
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络