A Modification on K-Nearest Neighbor Classifier

§ Iran University of Science and Technology (IUST) Iran University of Science and Technology (IUST)

Send Message

To: Author

A Modification on K-Nearest Neighbor Classifier

Article Fingerprint

ReserarchID

CSTB1R5F

A Modification on K-Nearest Neighbor Classifier Banner

AI TAKEAWAY

Connecting with the Eternal Ground
  • English
  • Afrikaans
  • Albanian
  • Amharic
  • Arabic
  • Armenian
  • Azerbaijani
  • Basque
  • Belarusian
  • Bengali
  • Bosnian
  • Bulgarian
  • Catalan
  • Cebuano
  • Chichewa
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Corsican
  • Croatian
  • Czech
  • Danish
  • Dutch
  • Esperanto
  • Estonian
  • Filipino
  • Finnish
  • French
  • Frisian
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian Creole
  • Hausa
  • Hawaiian
  • Hebrew
  • Hindi
  • Hmong
  • Hungarian
  • Icelandic
  • Igbo
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Khmer
  • Korean
  • Kurdish (Kurmanji)
  • Kyrgyz
  • Lao
  • Latin
  • Latvian
  • Lithuanian
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Maltese
  • Maori
  • Marathi
  • Mongolian
  • Myanmar (Burmese)
  • Nepali
  • Norwegian
  • Pashto
  • Persian
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Samoan
  • Scots Gaelic
  • Serbian
  • Sesotho
  • Shona
  • Sindhi
  • Sinhala
  • Slovak
  • Slovenian
  • Somali
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tajik
  • Tamil
  • Telugu
  • Thai
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Welsh
  • Xhosa
  • Yiddish
  • Yoruba
  • Zulu
Font Type
Font Size
Font Size
Bedground

Abstract

K-Nearest Neighbor (KNN) classification is one of the most fundamental and simple classification methods. When there is little or no prior knowledge about the distribution of the data, the KNN method should be one of the first choices for classification. In this paper a modification is taken to improve the performance of KNN. The main idea is to use robust neighbors in training data. This modified KNN is better from traditional KNN in both terms: robustness and performance. The proposed KNN classification is called Modified K-Nearest Neighbor (MKNN). Inspired from the traditional KNN algorithm, the main idea is to classify an input query according to the most frequent tag in set of neighbor tags. MKNN can be considered a kind of weighted KNN so that the query label is approximated by weighting the neighbors of the query. The procedure computes the frequencies of the same labeled neighbors to the total number of neighbors. The proposed method is evaluated on a variety of several standard UCI data sets. Experiments show the excellent improvement in the performance of KNN method.

References

24 Cites in Article
  1. Ludmila Kuncheva (2005). Combining Pattern Classifiers.
  2. Hamid Parvin,Hosein Alizadeh,Behrouz Minaei-Bidgoli,Morteza Analoui (2008). A Scalable Method for Improving the Performance of Classifiers in Multiclass Applications by Pairwise Classifiers and GA.
  3. H Parvin,H Alizadeh,M Moshki,B Minaei-Bidgoli,N Mozayani (2008). Divide & Conquer Classification and Optimization by Genetic Algorithm‖.
  4. H Parvin,H Alizadeh,B Minaei-Bidgoli,M Analoui,C Chr (2008). Combination of Classifiers using Heuristic Retraining‖.
  5. Hamid Parvin,Hosein Alizadeh,Behrouz Minaei-Bidgoli (2008). A New Approach to Improve the Vote-Based Classifier Selection.
  6. H Alizadeh,M Mohammadi,B Minaei-Bidgoli (2008). Ne ural Network Ensembles using Clustering Ensemble and Genetic Algorithm‖.
  7. H Parvin,H Alizadeh,B Minaei-Bidgoli (2009). A New Method for Constructing Classifier Ensembles.
  8. H Parvin,H Alizadeh,B Minaei-Bidgoli (2009). Using Clustering for Generating Diversity in Classifier Ensemble.
  9. B Darasay Nearest Neighbor pattern classification techniques.
  10. Evelyn Fix,J Hodges (1951). Discriminatory analysis: Nonparametric discrimination: Consistency properties.
  11. T Cover,P Hart (1967). Nearest neighbor pattern classification.
  12. Martin Hellman (1970). The Nearest Neighbor Classification Rule with a Reject Option.
  13. K Fukunaga,L Hostetler (1975). k-nearest-neighbor bayes-risk estimation.
  14. S Dudani (1976). The distance-weighted k-nearestneighbor rule.
  15. T Bailey,A Jain (1978). A note on distance-weighted knearest neighbor rules.
  16. S Bermejo,J Cabestany (2000). Adaptive soft k-nearestneighbour classifiers.
  17. A Jozwik (1983). A learning scheme for a fuzzy k-nn rule.
  18. James Keller,Michael Gray,James Givens (1985). A fuzzy K-nearest neighbor algorithm.
  19. K Itqon,I Shunichi,Satoru (2001). Improving Performance of k-Nearest Neighbor Classifier by Test Features.
  20. R Duda,P Hart,D Stork (2000). Pattern Classification.
  21. E Gose,R Johnsonbaugh,S Jost (1996). Pattern Recognition and Image Analysis.
  22. C Blake,C Merz (1998). Table 3: Health datasets from UCI machine learning repository..
  23. S Aeberhard,D Coomans,O Vel Comparison of Classifiers in High Dimensional Settings.
  24. Rousseauw (1983). Out in Africa - South African Gay and Lesbian Film Festival (Cape Town et al., South Africa).

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Dr.Hamid Parvin. 1970. "A Modification on K-Nearest Neighbor Classifier". Global Journal of Computer Science and Technology GJCST Volume 10 (GJCST Volume 10 Issue 14).

Download Citation

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
Version of record

v1.2

Issue date
October 14, 2010

Language
English
Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

Article Matrices
Total Views: 8.5K
Total Downloads: 439
All Trends

Request Access

Please fill out the form below to request access to this research paper. Your request will be reviewed by the editorial or author team.
X

This is the heading

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

High-quality academic research articles on global topics and journals.

A Modification on K-Nearest Neighbor Classifier

Dr.Hamid Parvin
Dr.Hamid Parvin Iran University of Science and Technology (IUST)