A multi-average based pseudo nearest neighbor classifier
Abstract
Conventional k nearest neighbor (KNN) rule is a simple yet effective method for classification, but its classification performance is easily degraded in the case of small size training samples with existing outliers. To address this issue, A multi-average based pseudo nearest neighbor classifier (MAPNN) rule is proposed. In the proposed MAPNN rule, k ( k − 1 ) / 2 (k > 1) local mean vectors of each class are obtained by taking the average of two points randomly from k nearest neighbors in every category, and then k pseudo nearest neighbors are chosen from k ( k − 1 ) / 2 local mean neighbors of every class to determine the category of a query point. The selected k pseudo nearest neighbors can reduce the negative impact of outliers in some degree. Extensive experiments are carried out on twenty-one numerical real data sets and four artificial data sets by comparing MAPNN to other five KNN-based methods. The experimental results demonstrate that the proposed MAPNN is effective for classification task and achieves better classification results in the small-size samples cases comparing to five relative KNN-based classifiers.
References
[1]
J. Alcalá-Fdez, A. Fernández, J. Luengo, J. Derrac, S. Garála, L. Sánchez and F. Herrera, Keel data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework, Journal of Multiple-Valued Logic and Soft Computing 17 (2011), 255–287.
[2]
K. Bache and M. Lichman, UCI Machine Learning Repository, 2013, http://archive.ics.uci.edu/ml/.
[3]
T. Cover and P. Hart, Nearest neighbor pattern classification, Journal of Robotics and Control 13(1) (1967), 21–27.
[4]
J. Demšar, Statistical comparisons of classifiers over multiple data sets, Journal of Machine Learning Research 7 (2006), 1–30.
[5]
J. Derrac, S. Garc’ia, D. Molina and F. Herrera, A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms, Swarm and Evolutionary Computation 1(1) (2011), 3–18.
[6]
S.A. Dudani, The distance-weighted k-nearest-neighbor rule, IEEE Transactions on Systems, Man, and Cybernetics 6(4) (1976), 325–327.
[7]
K. Fukunaga, Introduction to Statistical Pattern Recognition, 2th edn, Academic Press, San Diego, 1990.
[8]
S. Garcia and F. Herrera, An extension on statistical comparisons of classifiers over multiple datasets for all pairwise comparisons, Journal of Machine Learning Research 9(12) (2008), 2677–2694.
[9]
J. Gou, H. Ma, W. Ou, S. Zeng, Y. Rao and H. Yang, A generalized mean distance-based k-nearest neighbor classifier, Expert Systems With Applications 115 (2019), 356–372.
[10]
J. Gou, W. Qiu, Y. Zhang, X. Shen, Y. Zhan and W. Ou, Locality constrained representation-based K-nearest neighbor classification, Knowledge-Based Systems 167(1) (2019), 38–52.
[11]
J. Gou, W. Qiu, Y. Zhang, Y. Xu, Q. Mao and Y. Zhan, A local mean representation-based K-nearest neighbor classifier, ACM Transactions on Intelligent Systems and Technology 10(3) (2019), 1–25.
[12]
J. Gou, W. Qiu, Y. Zhang, Y. Xu, Q. Mao and Y. Zhan, A local mean representation-based K-nearest neighbor classifier, ACM Transactions on Intelligent Systems and Technology 10(3) (2019), 1–25.
[13]
J. Gou, L. Sun, L. Du, H. Ma, T. Xiong and W. Ou, A representation coefficient-based k-nearest centroid neighbor classifier, Expert Systems With Applications 194(15) (2022), 38–52.
[14]
J. Gou, Y. Zhan, Y. Rao, X. Shen, X. Wang and W. He, Improved pseudo nearest neighbor classification, Knowledge-Based Systems 70 (2014), 361–375.
[15]
J. Gou, Y. Zhang, L. Du and T. Xiong, A local mean-based k-nearest centroid neighbor classifier, Computer Journal 55(9) (2012), 1058–1071.
[16]
B. Li, Y.W. Chen and Y.Q. Chen, The nearest neighbor algorithm of local probability centers, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 38(1) (2008), 141–154.
[17]
Y. Ma, R. Huang, M. Yan, G. Li and T. Wang, Attention-based local mean k-nearest centroid neighbor classifier, Pattern Recognition 201(1) (2022).
[18]
S. Memis, S. Enginoglu and U. Erkan, Fuzzy parameterized fuzzy soft k-nearest neighbor classifier, Neurocomputing 500 (2022), 351–378.
[19]
Y. Mitani and Y. Hamamoto, A local mean-based nonparametric classifier, Pattern Recognition Letters 27(10) (2006), 1151–1159.
[20]
J.V. Ness, On the dominance of non-parametric Bayes rule discriminant algorithms in high dimensions, Pattern Recognition 12(6) (1980), 355–368.
[21]
Z. Pan, Y. Pan, Y. Wang and W. Wang, A new globally adaptive k-nearest neighbor classifier based on local mean optimization, Soft Computing 25 (2021), 2417–2431.
[22]
Z. Pan, Y. Wang and W. Ku, A new k-harmonic nearest neighbor classifier based on the multi-local means, Expert Systems with Applications 67 (2017), 115–125.
[23]
X. Wu, V. Kumar, J.R. Quinlan, J. Ghosh, Q. Yang, H. Motoda and Z. Zhou, Top 10 algorithms in data mining, Knowledge Information System 14(1) (2008), 1–37.
[24]
Y. Zeng, Y. Yang and L. Zhao, Nonparametric classification based on local mean and class statistics, Expert Systems with Applications 36(4) (2009), 8443–8448.
[25]
Y. Zeng, Y. Yang and L. Zhao, Pseudo nearest neighbor rule for pattern classification, Expert Systems with Applications 36(2) (2009), 3587–3595.
[26]
S. Zhang, Challenges in KNN classification, IEEE Transactions on Knowledge and Data Engineering 34(10) (2021), 4663–4675.
[27]
S. Zhang, X. Li, M. Zong, X. Zhu and R. Wang, Efficient kNN classification with different numbers of nearest neighbors, IEEE Transactions on Neural Networks and Learning Systems 29(5) (2019), 1774–1785.
Index Terms
- A multi-average based pseudo nearest neighbor classifier
Index terms have been assigned to the content through auto-classification.
Recommendations
Local Mean k-General Nearest Neighbor Classifier
ICSCA '21: Proceedings of the 2021 10th International Conference on Software and Computer ApplicationsThe well-known k-Nearest Neighbor classifier is a simple and flexible algorithm that has sparked wide interest in pattern classification. In spite of its straightforward implementation, the kNN is sensitive to the presence of noisy training samples and ...
A pseudo nearest centroid neighbour classifier
In this paper, we propose a new reliable classification approach, called the pseudo nearest centroid neighbour rule, which is based on the pseudo nearest neighbour rule PNN and nearest centroid neighbourhood NCN. In the proposed PNCN, the nearest ...
Improved pseudo nearest neighbor classification
k-Nearest neighbor (KNN) rule is a very simple and powerful classification algorithm. In this article, we propose a new KNN-based classifier, called the local mean-based pseudo nearest neighbor (LMPNN) rule. It is motivated by the local mean-based k-...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
© 2024 – IOS Press. All rights reserved.
Publisher
IOS Press
Netherlands
Publication History
Published: 18 September 2024
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 05 Jan 2025