Hybrid κ-Nearest Neighbor Classifier

Zhiwen Yu, Hantao Chen, Jiming LIU, Jane You, Hareton Leung, Guoqiang Han

Research output: Contribution to journalJournal articlepeer-review

105 Citations (Scopus)


Conventional κ-nearest neighbor (KNN) classification approaches have several limitations when dealing with some problems caused by the special datasets, such as the sparse problem, the imbalance problem, and the noise problem. In this paper, we first perform a brief survey on the recent progress of the KNN classification approaches. Then, the hybrid KNN (HBKNN) classification approach, which takes into account the local and global information of the query sample, is designed to address the problems raised from the special datasets. In the following, the random subspace ensemble framework based on HBKNN (RS-HBKNN) classifier is proposed to perform classification on the datasets with noisy attributes in the high-dimensional space. Finally, the nonparametric tests are proposed to be adopted to compare the proposed method with other classification approaches over multiple datasets. The experiments on the real-world datasets from the Knowledge Extraction based on Evolutionary Learning dataset repository demonstrate that RS-HBKNN works well on real datasets, and outperforms most of the state-of-The-Art classification approaches.

Original languageEnglish
Article number7137658
Pages (from-to)1263-1275
Number of pages13
JournalIEEE Transactions on Cybernetics
Issue number6
Publication statusPublished - Jun 2016

Scopus Subject Areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

User-Defined Keywords

  • Classification
  • ensemble learning
  • machine learning
  • nearest neighbor classifier
  • supervised learning


Dive into the research topics of 'Hybrid κ-Nearest Neighbor Classifier'. Together they form a unique fingerprint.

Cite this