Abstract:
KNN is a popular data mining algorithm. It has been successfully used for data analysis in many computer science research areas.
This paper shows that KNN classification, despite its success, faces many challenges, including K computation, nearest neighbor selection, nearest neighbor search, and classification rules.
After establishing these issues, recent approaches to their resolution are examined in more detail, providing a potential roadmap for KNN-related research and new classification rules for training sample imbalance. 15 UCI benchmark datasets were used to test the approaches.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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