Call for Paper - May 2019 Edition
IJCA solicits original research papers for the May 2019 Edition. Last date of manuscript submission is April 20, 2019. Read More

Evaluating the Performance of Dual Weighted K- Nearest Neighbor Classifier

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Pooja Rani

Pooja Rani. Evaluating the Performance of Dual Weighted K- Nearest Neighbor Classifier. International Journal of Computer Applications 179(36):29-35, April 2018. BibTeX

	author = {Pooja Rani},
	title = {Evaluating the Performance of Dual Weighted K- Nearest Neighbor Classifier},
	journal = {International Journal of Computer Applications},
	issue_date = {April 2018},
	volume = {179},
	number = {36},
	month = {Apr},
	year = {2018},
	issn = {0975-8887},
	pages = {29-35},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2018916818},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


A non-parametric, very simple to use, effective instance-based learning algorithm called K-Nearest Neighbor (KNN), is most widely used to classify the objects in data mining. KNN has some shortcomings which affect its classification performance like the equal impact of all attributes, curse of dimensionality, the value of ‘k' parameter and simple voting. A variety of techniques are developed in literature to get better performance. This paper presents an improved algorithm called dual weighted KNN that is a combination of attribute weighted and instance weighted techniques. To verify the performance of proposed algorithm it is conducted on fourteen different datasets taken from UCI in ‘R’ data mining tool. The results show that the proposed algorithm significantly outperforms than traditional KNN, Attribute weighted KNN and Instance weighted KNN.


  1. J. Han and M. Kamber, Data mining: concepts and techniques, 2nd ed. Amsterdam ; Boston : San Francisco, CA: Elsevier, 2006.
  2. S. Taneja, C. Gupta, K. Goyal, and D. Gureja, “An Enhanced K-Nearest Neighbor Algorithm Using Information Gain and Clustering,” presented at the Fourth International Conference on Advanced Computing & Communication Technologies, 2014, pp. 325–329, 2014.
  3. S. B. Imandoust and M. Bolandraftar, “Application of k-nearest neighbor (knn) approach for predicting economic events: Theoretical background,” Int. J. Eng. Res. Appl., vol. 3, no. 5, pp. 605–610, 2013.
  4. D. Wettschereck, D. W. Aha, and T. Mohri, “A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms,” in Lazy learning, Springer, 1997, pp. 273–314, 1997.
  5. Z. Li, Z. Chengjin, X. Qingyang, and L. Chunfa, "Weighted-KNN and its application on UCI," presented at the International Conference on Information and Automation, 2015, pp. 1748–1750, 2015.
  6. Ming Zhao and Jingchao Chen, “Improvement and Comparision of Weighted K-Nearest-Neighbors Classifiers for Model Selection,” J. Softw. Eng., pp. 1–10, 2016.
  7. L. Jiang, Z. Cai, D. Wang, and S. Jiang, “Survey of improving k-nearest-neighbor for classification,” presented at the Fourth International Conference on Fuzzy Systems and Knowledge Discovery, 2007, vol. 1, pp. 679–683, 2007.
  8. M. E. Syed, “Attribute weighting in k-nearest neighbor classification,” University of Tampere, 2014.
  9. J. Gou, L. Du, Y. Zhang, T. Xiong, and others, “A new distance-weighted k-nearest neighbor classifier,” J Inf Comput Sci, vol. 9, no. 6, pp. 1429–1436, 2012.
  10. K. Hechenbichler and K. Schliep, "Weighted k-nearest-neighbor techniques and ordinal classification," Institue for Statistik sonderforschungsbereich, 386, 2004.
  11. L. Jiang, H. Zhang, and Z. Cai, "Dynamic k-nearest-neighbor naive Bayes with attribute weighted," in International Conference on Fuzzy Systems and Knowledge Discovery, 2006, pp. 365–368, 2006.
  12. D. P. Vivencio, E. R. Hruschka, M. do Carmo Nicoletti, E. B. dos Santos, and S. D. Galvao, “Feature-weighted k-nearest neighbor classifier,” presented at the Foundations of Computational Intelligence,2007, pp. 481–486, 2007.
  13. W. Baobao, M. Jinsheng, and S. Minru, “An enhancement of K-Nearest Neighbor algorithm using information gain and extension relativity,” presented at the International Conference on Condition Monitoring and Diagnosis, 2008, pp. 1314–1317, 2008.
  14. X. Xiao and H. Ding, "Enhancement of K-nearest neighbor algorithm based on the weighted entropy of attribute value," presented at the Fourth International Conference on Advanced & Communication Technologies, 2012, pp. 1261–1264, 2012.
  15. X. Li and C. Xiang, “Correlation-based K-nearest neighbor algorithm,” presented at the 3rd International Conference on Software Engineering and Service Science, 2012, pp. 185–187, 2012.
  16. J. Wu, Z. Hua Cai, and S. Ao, "Hybrid dynamic k-nearest-neighbor and distance and attribute weighted method for classification," Int. J. Comput. Appl. Technol., vol. 43, no. 4, pp. 378–384, 2012.


KNN, Attribute weighted KNN, Instance weighted KNN, and Distance weighted KNN.