Locally adaptive k parameter selection for nearest neighbor classifier: one nearest cluster

1. Myatt G (2007) Making sense of data: a practical guide to exploratory data analysis and data mining , p 176–181 Wiley, New York

2. Bache K, Lichman M (2013) UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences. http://archive.ics.uci.edu/ml

3. Ho T, Basu M (2002) Complexity measures of supervised classification problems. IEEE Trans Pattern Anal Mach Intell 24(3):289–300

   Article  Google Scholar 

4. Bhatia N (2010) Survey of nearest neighbor techniques. Int J Comput Sci Inf Secur 8(2):302–305

   Google Scholar 

5. Jiang L, Cai Z, Wang D, Jiang S (2007) Survey of improving K-nearest-neighbor for classification. In: Fourth international conference on fuzzy systems and knowledge discovery, vol 1, pp 679–683. doi:10.1109/FSKD.2007.552

6. Miloud-Aouidate A, Baba-Ali AR (2011) Survey of nearest neighbor condensing techniques. Int J Adv Comput Sci Appl 2(11):59–64

   Google Scholar 

7. Alexandros A, Michael O, Anthony B (2013) Adaptive distance metrics for nearest neighbour classification based on genetic programming. Lecture notes in computer science, vol 7831, Springer, Heidelberg, pp 1–12

8. Wang J, Neskovic P, Cooper LN (2007) Improving nearest neighbor rule with a simple adaptive distance measure. Pattern Recognit Lett 28(2):207–213

   Article  Google Scholar 

9. Fandos R, Debes C, Zoubir AM (2013) Resampling methods for quality assessment of classifier performance and optimal number of features. Signal Process 93:2956–2968. doi:10.1016/j.sigpro.2013.05.004

   Article  Google Scholar 

10. Ghosh AK, Chaudhuri P, Murthy CA (2006) Multi-scale classification using nearest neighbor density estimates. IEEE Trans Syst Man Cybern Part B 36(5):1139–1148

    Article  Google Scholar 

11. Ozger ZB and Amasyali M (2013) KNN parameter selection via meta learning. In: Signal processing and communications applications conference (SIU), Trabzon, Turkey. doi:10.1109/SIU.2013.6531231

12. Sanchez J, Pla F, Ferri F (1997) On the use of neighbourhood-based non-parametric classifiers. Pattern Recognit Lett 18(11–13):1179–1186

    Article  Google Scholar 

13. Ghosh AK (2006) On optimum choice of k in nearest neighbor classification. Comput Stat Data Anal 50(11):3113–3123

    Article  MathSciNet  MATH  Google Scholar 

14. Guo G, Wang H, Bell D, Bi Y, Greer K (2003) KNN model-based approach in classification. Lecture notes in computer science, vol 2888, pp 986–996. doi:10.1007/978-3-540-39964-3_62

15. Ghosh AK (2007) On nearest neighbor classification using adaptive choice of k. J Comput Gr Stat 16(2):482–502

    Article  MathSciNet  Google Scholar 

16. Wang H, Nie F, Huang H (2014) Robust distance metric learning via simultaneous l1-norm minimization and maximization. In: Proceedings of the 31st international conference on machine, JMLR: W&CP, vol 32. Beijing, China.

17. Xiang S, Nie F, Zhang C (2008) Learning a Mahalanobis distance metric for data clustering and classification. Pattern Recognit 41:3600–3612

    Article  MATH  Google Scholar 

18. KD-tree searcher class, MathWorks. www.mathworks.com/help/stats/kdtreesearcher-class.html. Accessed 2014 Oct 22

19. Weiss MA (2013) Data structures & algorithm analysis in C++, 4th edn. Pearson, London, pp 83–85, 614–618, 629

20. Myatt G (2007) Making sense of data: a practical guide to exploratory data analysis and data mining. Wiley, New York, pp 120–129

    Book  MATH  Google Scholar 

21. Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MathSciNet  MATH  Google Scholar 

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