Even though finding out distance is the central core of k-Nearest Neighbor classification techniques, similarity measures are often favored against distance in various realistic scenarios and situation. Most of the similarity measures, which are used to classify an instance, are based on geometric model. Their effectiveness decreases with the increases in the number of dimensions. This paper establishes an efficient technique called ARSkNN for finding out class of any given instance using a measure based on an unique similarity, that does no longer compute distance, for k-NN classification. Our empirical results show that ARSkNN classification technique is better than the previous established k-NN classifiers. The performance of algorithm was verified and validated on various datasets from different domains.
AhaD.W.Lazy learning, chapter Lazy Learning, pp. 7–10. Kluwer Academic Publishers, Norwell. MA, USA, 1997.
2.
AlimogluF. and AlpaydinE., Methods of combining multiple classifiers based on different representations for penbased handwritten digit recognition, In. Proceedings of the Fifth Turkish Artificial Intelligence and Artificial Neural Networks Symposium (TAINN 96.Citeseer1996.
3.
BaoliL., QinL. and ShiwenY., An adaptive k-nearest neighbor text categorization strategy, ACM Transactions on Asian Language Information Processing (TALIP)3(4) (2004), 215–226.
4.
BlackardJ.A. and DeanD.J., Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables, Computers and Electronics in Agriculture24(3) (1999), 131–151.
5.
BockR.K., ChilingarianA., GaugM., HaklF., HengstebeckTh., JiřinaM., KlaschkaJ., KotrčE., SckýP., TowersS., ., Methods for multidimensional event classification: A case study using images from a cherenkov gamma-ray telescope. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment516(2) (2004), 511–528.
6.
BroelemannK., JiangX., MukherjeeS. and ChowdhuryA.S., Variants of k-regular nearest neighbor graph and their construction, Information Processing Letters (2017).
7.
ChaS.-H., Comprehensive survey on distance/similarity measures between probability density functions, City1(2) (2007), 1.
8.
ChenS., MaB. and ZhangK., On the similarity metric and the distance metric, Theoretical Computer Science410(24) (2009), 2365–2376.
9.
ChenY.-S., HungY.-P., YenT.-F. and FuhC.-S., Fast and versatile algorithm for nearest neighbor search based on a lower bound tree, Pattern Recognition40(2) (2007), 360–375.
10.
ChienJ.-T. and WuC.-C., Discriminant wavelet faces and nearest feature classifiers for face recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence24(12) (2002), 1644–1649.
11.
ChoiS.-S., ChaS.-H. and TappertC.C., A survey of binary similarity and distance measures, Journal of Systemics, Cybernetics and Informatics8(1) (2010), 43–48.
12.
CoverT. and HartP., Nearest neighbor pattern classification, IEEE Transactions on Information Theory13(1) (1967), 21–27.
13.
FrancoisD., WertzV. and VerleysenM., The concentration of fractional distances, IEEE Transactions on Knowledge and Data Engineering19(7) (2007), 873–886.
14.
FreyP.W. and SlateD.J., Letter recognition using holland-style adaptive classifiers, Machine Learning6(2) (1991), 161–182.
15.
GagnéC. and ParizeauM., Coevolution of nearest neighbor classifiers, International Journal of Pattern Recognition and Artificial Intelligence21(05) (2007), 921–946.
HallM., FrankE., HolmesG., PfahringerB., ReutemannP. and WittenI.H., The weka data mining software: An update, ACM SIGKDD Explorations Newsletter11(1) (2009), 10–18.
18.
KumarA., BhatnagarR. and SrivastavaS., Arsknn-a k-nn classifier using mass based similarity measure, Procedia Computer Science46 (2015), 457–462.
19.
LiS.Z. and LuJ., Face recognition using the nearest feature line method, IEEE Transactions on Neural Networks10(2) (1999), 439–443.
20.
NohY.-K., ZhangB.-T. and LeeD.D., Generative local metric learning for nearest neighbor classification. In Advances in Neural Information Processing Systems (2010), pp. 1822–1830.
21.
OmercevicD., DrbohlavO. and LeonardisA., High-dimensional feature matching: Employing the concept of meaningful nearest neighbors, In 2007 IEEE 11th International Conference on Computer Vision (2007). pp. 1–8. IEEE.
22.
QamarA.M., GaussierE., ChevalletJ.-P. and LimJ.H., Similarity learning for nearest neighbor classification. In 2008 Eighth IEEE International Conference on Data Mining2008, pp. 983–988IEEE.
23.
SametH., K-nearest neighbor finding using maxnearest-dist, IEEE Transactions on Pattern Analysis and Machine Intelligence30(2) (2008), 243–252.
24.
SamworthR.J.., Optimal weighted nearest neighbour classifiers, The Annals of Statistics40(5) (2012), 2733–2763.
25.
Shalev-ShwartzS., SingerY. and NgA.Y., Online and batch learning of pseudo-metrics. In Proceedings of the twenty-first international conference on Machine learning (2004) pp. 94. ACM.
26.
SkopalT. and BustosB., On nonmetric similarity search problems in complex domains, ACM Computing Surveys (CSUR)43(4) (2011), 34.
27.
TheodoridisS., KoutroumbasK.Pattern recognition, academic press. New York, 1999.
28.
TingK.M., WashioT., WellsJ.R., LiuF.T. and AryalS., Demass: A new density estimator for big data, Knowledge and Information Systems35(3) (2013), 493–524.
29.
TingK.M., ZhouG.-T., LiuF.T., TanJ.S.C.Mass estimation and its applications. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010, pp. 989–998. ACM.
30.
TingK.M., ZhouG.-T., LiuF.T. and TanS.C., Mass estimation, Machine Learning90(1) (2013), 127–160.
31.
TingK.M., FernandoT.L., WebbG.I.Mass-based similarity measure: An effective alternative to distance-based similarity measures. Technical report, Technical report 2013/276, Calyton School of IT, Monash University, Australia, 2013.
32.
VaidyaP.M., Ano (n logn) algorithm for the all-nearestneighbors problem. Discrete & Computational Geometry4(2) (1989), 101–115.
33.
WangJ., NeskovicP. and CooperL.N., Improving nearest neighbor rule with a simple adaptive distance measure. Pattern Recognition Letters28(2) (2007), 207–213.
34.
WeinbergerK.Q. and SaulL.K., Distance metric learning for large margin nearest neighbor classification, Journal of Machine Learning Research10(Feb) (2009), 207–244.
35.
XiongY.-J., LuY. and WangP.S.P., Off-line text-independent writer recognition: A survey, International Journal of Pattern Recognition and Artificial Intelligence31(05) (2017), 1756008.
36.
ZezulaP., AmatoG., DohnalV., BatkoM.Similarity search: The metric space approach, volume 32. Springer Science & Business Media, 2006.
37.
ZhengW., ZhaoL. and ZouC., Locally nearest neighbor classifiers for pattern classification, Pattern Recognition37(6) (2004), 1307–1309.
