ArticlePeerJ. Computer science2019
The k conditional nearest neighbor algorithm for classification and class probability estimation.
Article in PeerJ. Computer science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Measurement and classification of dielectric properties in human brain tissues: differentiating glioma from normal tissues using machine learning.Physical and engineering sciences in medicine · 2026Article
- Double weighted k nearest neighbours for binary classification of high dimensional genomic data.Scientific reports · 2025Article
- Random k conditional nearest neighbor for high-dimensional data.PeerJ. Computer science · 2025Article
- A Machine Learning Approach to Assess Patients with Deep Neck Infection Progression to Descending Mediastinitis: Preliminary Results.Diagnostics (Basel, Switzerland) · 2023Article
- Swarm intelligence-based model for improving prediction performance of low-expectation teams in educational software engineering projects.PeerJ. Computer science · 2022Article
- Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network.Sensors (Basel, Switzerland) · 2021Article
- Optimal 1-NN prototypes for pathological geometries.PeerJ. Computer science · 2021Article
- Boolean logic algebra driven similarity measure for text based applications.PeerJ. Computer science · 2021Article
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Authors and funding
3 authors.
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Abstract
The k nearest neighbor (kNN) approach is a simple and effective nonparametric algorithm for classification. One of the drawbacks of kNN is that the method can only give coarse estimates of class probabilities, particularly for low values of k. To avoid this drawback, we propose a new nonparametric classification method based on nearest neighbors conditional on each class: the proposed approach calculates the distance between a new instance and the kth nearest neighbor from each class, estimates posterior probabilities of class memberships using the distances, and assigns the instance to the class with the largest posterior. We prove that the proposed approach converges to the Bayes classifier as the size of the training data increases. Further, we extend the proposed approach to an ensemble method. Experiments on benchmark data sets show that both the proposed approach and the ensemble version of the proposed approach on average outperform kNN, weighted kNN, probabilistic kNN and two similar algorithms (LMkNN and MLM-kHNN) in terms of the error rate. A simulation shows that kCNN may be useful for estimating posterior probabilities when the class distributions overlap.
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