International Journal of Scientific Inventions and Innovations (IJSII)

Title : An Analysis on the Performance of a K-Nearest-Neighbor Classification Based Outlier Detection System using Feature Selection and Dimensionality Reduction Techniques

Authors : Kurian M. J and Dr. Gladston Raj S     Volume 1 Issue 1    Pages: 1 - 7

ABSTRACT - The general idea of classification-based outlier detection method is to train a classification model that can distinguish normal data from outliers. In the previous work, we have implemented and evaluated three classification based outlier detection algorithms and found that the k-neighborhood algorithm was capable of identifying and classifying the outliers better than the other two compared algorithm in terms of accuracy, f-score, Sensitivity/Recall, error rate. Further, the cpu time of the k-neighborhood algorithm also minimum. In this work, the performance of outlier detection is evaluated using dimensionality reduction algorithms. The results clearly shows that the impact of dimensionality reduction algorithm on the cancer dataset is significantly improved the overall classification performance to a considerable level.

References

1. Simon Hawkins, Hongxing He, Graham Williams and Rohan Baxter, “Outlier Detection Using Replicator Neural Networks, DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery Pages 170-180
2. Graham Williams, Rohan Baxter, Hongxing He, Simon Hawkins and Lifang Gu, “A Comparative Study of RNN for Outlier Detection in Data Mining”, ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining, Page 709.
3. Hodge, V.J. and Austin, J. (2004) A survey of outlier detection methodologies. Artificial Intelligence Review, 22 (2). pp. 85-126.
4. A. Faizah Shaari, B. Azuraliza Abu Bakar, C. Abdul Razak Hamdan, "On New Approach in Mining Outlier" Proceedings of the International Conference on Electrical Engineering and Informatics, Indonesia June 17-19, 2007
5. Yumin Chen, Duoqian Miao, Hongyun Zhang, "Neighborhood outlier detection", Expert Systems with Applications 37 (2010) 8745-8749, 2010 Elsevier
6. Xiaochun Wang, Xia Li Wang, D. Mitch Wilkes, “A Minimum Spanning Tree-Inspired Clustering-Based Outlier Detection Technique”, Advances in Data Mining. Applications and Theoretical Aspects, Lecture Notes in Computer Science Volume 7377, 2012, pp 209-223
7. Jiawei Han, Micheline Kamber and Jian Pei, "Data Mining Concepts and Techniques (Third Edition)", Morgan Kaufmann Publishers is an imprint of Elsevier, c 2012 by Elsevier Inc.
8. D.Lavanya, Dr.K.Usha Rani,..," Analysis of feature selection with classification: Breast cancer datasets",Indian Journal of Computer Science and Engineering (IJCSE),October 2011.
9. E.Osuna, R.Freund, and F. Girosi, "Training support vector machines: Application to face detection". Proceedings of computer vision and pattern recognition, Puerto Rico pp. 130-136.1997.
10. Vaibhav Narayan Chunekar, Hemant P. Ambulgekar (2009). Approach of Neural Network to Diagnose Breast Cancer on three different Data Set. 2009 International Conference on Advances in Recent Technologies in Communication and Computing.
11. D. Lavanya, "Ensemble Decision Tree Classifier for Breast Cancer Data," International Journal of Information Technology Convergence and Services, vol. 2, no. 1, pp. 17-24, Feb. 2012.
12. B.Ster, and A.Dobnikar, "Neural networks in medical diagnosis: Comparison with other methods." Proceedings of the international conference on engineering applications of neural networks pp. 427-430. 1996.
13. T.Joachims, Transductive inference for text classification using support vector machines. Proceedings of international conference machine learning. Slovenia. 1999.
14. J.Abonyi, and F. Szeifert, "Supervised fuzzy clustering for the identification of fuzzy classifiers." Pattern Recognition Letters, vol.14(24), 2195-2207,2003.
15. Frank, A. & Asuncion, A. (2010). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
16. Street WN, Wolberg WH, Mangasarian OL. Nuclear feature extraction for breast tumor diagnosis. Proceedings IS&T/ SPIE International Symposium on Electronic Imaging 1993; 1905:861-70.
17. William H. Wolberg, M.D., W. Nick Street, Ph.D., Dennis M. Heisey, Ph.D., Olvi L. Mangasarian, Ph.D. computerized breast cancer diagnosis and prognosis from fine needle aspirates, Western Surgical Association meeting in Palm Desert, California, November 14, 1994.
18. Chen, Y., Abraham, A., Yang, B.(2006), Feature Selection and Classification using Flexible Neural Tree. Journal of Neurocomputing 70(1-3): 305-313.
19. J. Han and M. Kamber,"Data Mining Concepts and Techniques", Morgan Kauffman Publishers, 2000.
20. Duda, R.O., Hart, P.E.: "Pattern Classification and Scene Analysis", In: Wiley-Interscience Publication, New York (1973)
21. Bishop, C.M.: "Neural Networks for Pattern Recognition". Oxford University Press,New York (1999).
22. Vapnik, V.N., The Nature of Statistical Learning Theory, 1st ed., Springer-Verlag,New York, 1995.
23. Ross Quinlan, (1993) C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, San Mateo, CA.
24. Cabena, P., Hadjinian, P., Stadler, R., Verhees, J. and Zanasi, A. (1998). Discovering Data Mining: From Concept to Implementation, Upper Saddle River, N.J., Prentice Hall.
25. Kurian M.J ,Dr. Gladston Raj S. “Outlier Detection in Multidimensional Cancer Data using Classification Based Appoach” International Journal of Advanced Engineering Research(IJAER) Vol. 10 ,No.79 , pp –(342 348) 2015.
26. Kurian M.J , Dr. Gladston Raj S. “ An Analysis on the Performance of a Classification Based Outlier Detection System using Feature Selection” International Journal of Computer Applications (IJCA) Vol.132.No.8. December 2015.