Efficient kNN classification algorithm for big data

By Zhenyun Deng, Xiaoshu Zhu, Debo Cheng, Ming Zong, Shichao Zhang. Neurocomputing, 2016

529 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b91334db900b1c066ed5c811c9e5eea57a7e7b08

Citations in the corpus

• Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review (2017, unknown): “Despite its slow characteristic, surprisingly, KNN is used extensively for Big data classification, this includes the works of [89, 90, 91, 92, 93], this is due to the other good characteristics of the KNN such as simplicity and reasonable accuracy, since the speed issue is normally solved using som”
• Efficient kNN Classification With Different Numbers of Nearest Neighbors (2018, unknown): “6) Landmark-based spectral Clustering kNN (LC-kNN) [64] was proposed to first conduct k-means clustering to separate the whole data set into several parts and then select the nearest cluster as the training samples for conducting the kNN classification.”
• A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions (2020, unknown): “Hence, obtaining the optimal value of k through testing is vital for the accuracy of this algorithm [115].”