Challenges in KNN Classification 论文

2021IEEE Transactions on Knowledge and Data Engineering引用 336

Machine Learning and Data ClassificationImbalanced Data Classification TechniquesAnomaly Detection Techniques and Applications

人工智能 Machine Learning and Data Classification Anomaly Detection Techniques and Applications Imbalanced Data Classification Techniques

关系图谱

作者

摘要

The KNN algorithm is one of the most popular data mining algorithms. It has been widely and successfully applied to data analysis applications across a variety of research topics in computer science. This paper illustrates that, despite its success, there remain many challenges in KNN classification, including K computation, nearest neighbor selection, nearest neighbor search and classification rules. Having established these issues, recent approaches to their resolution are examined in more detail, thereby providing a potential roadmap for ongoing KNN-related research, as well as some new classification rules regarding how to tackle the issue of training sample imbalance. To evaluate the proposed approaches, some experiments were conducted with 15 UCI benchmark datasets.

作者查看全部 (1)

Shichao Zhang

Challenges in KNN Classification 论文

详细信息

摘要

作者查看全部 (1)

相关技术查看全部 (3)

相关事件

相关文章