Classification Based Outlier Detection Techniques
| ||International Journal of Computer Trends and Technology (IJCTT)|| |
|© - Issue 2012 by IJCTT Journal|
|Volume-3 Issue-2 |
|Year of Publication : 2012|
|Authors :Dr. Shuchita Upadhyaya, Karanjit Singh.|
Dr. Shuchita Upadhyaya, Karanjit Singh."Classification Based Outlier Detection Techniques"International Journal of Computer Trends and Technology (IJCTT),V3(2):290-294 Issue 2012 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: -Outlier detection is an important research area forming part of many application domains. Specific application domains call for specific detection techniques, while the more generic ones can be applied in a large number of scenarios with good results. This survey tries to provide a structured and comprehensive overview of the research on Classification Based Outlier Detection listing out various techniques as applicable to our area of research. We have focused on the underlying approach adopted by each technique. We have identified key assumptions, which are used by the techniques to differentiate between normal and Outlier behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. We provide a basic outlier detection technique, and then show how the different existing techniques in that category are variants of this basic technique. This template provides an easier and succinct understanding of the Classification based techniques. Further we identify the advantages and disadvantages of various classification based techniques. We also provide a discussion on the computational complexity of the techniques since it is an important issue in our application domain. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in this area can be applied in other domains for which they were not intended to begin with.
 Tan, P.-N., Steinbach, M., and Kumar, V. 2005. Introduction to Data Mining. Addison-Wesley.
 Duda, R. O., Hart, P. E., and Stork, D. G. 2000. Pattern Classification (2nd Edition). Wiley-Interscience.
 Stefano, C., Sansone, C., and Vento, M. 2000. To reject or not to reject: that is the question - an answer in case of neural classifiers. IEEE Transactions on Systems, Management and Cybernetics 30, 1, 84 - 94.
 Barbara, D., Couto, J., Jajodia, S., and Wu, N. 2001b. Detecting novel network intrusions using bayes estimators. In Proceedings of the First SIAM International Conference on Data Mining.
 Scholkopf,Ä B., Platt, J. C., Shawe-Taylor, J. C., Smola, A. J., and Williamson, R. C. 2001. Estimating the support of a high-dimensional distribution. Neural Comput. 13, 7, 1443 - 1471.
 Roth, V. 2004. Outlier detection with one-class kernel fisher discriminants.
 Roth, V. 2006. Kernel fisher discriminants for outlier detection. Neural Computation 18, 4, 942 - 960.
 Odin, T. and Addison, D. 2000. Novelty detection using neural network technology. In Proceedings of the COMADEN Conference. Houston, TX.
 Ghosh, A. K., Schwartzbard, A., and Schatz, M. 1999a. Learning program behavior profiles for intrusion detection. In Proceedings of 1st USENIX Workshop on Intrusion Detection and Network Monitoring. 51 - 62.
 Ghosh, A. K., Wanken, J., and Charron, F. 1998. Detecting anomalous and unknown intrusions against programs. In Proceedings of the 14th Annual Computer Security Applications Conference. IEEE Computer Society, 259.
 Barson, P., Davey, N., Field, S. D. H., Frank, R. J., and McAskie, G. 1996. The detection of fraud in mobile phone networks. Neural Network World 6, 4.
 Hickinbotham, S. J. and Austin, J. 2000b. Novelty detection in airframe strain data. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. Vol. 6. 24 - 27.
 Bishop, C. 1994. Novelty detection and neural network validation. In Proceedings of IEEE Vision, Image and Signal Processing. Vol. 141. 217 - 222.
 Ghosh, S. and Reilly, D. L. 1994. Credit card fraud detection with a neural-network. In Proceedings of the 27th Annual Hawaii International Conference on System Science. Vol. 3. Los Alamitos, CA.
 Jakubek, S. and Strasser, T. 2002. Fault-diagnosis using neural networks with ellipsoidal basis functions. In Proceedings of the American Control Conference. Vol. 5. 3846 - 3851.
Keywords —Outliers, Classification, Outlier Detection, Classification based Outlier Detection, One-Class, Multi-Class, Algorithms, Data Mining.