Enhancing the quality of noisy training data using a genetic algorithm and prototype selection

Proceedings of the 2008 International Conference on Artificial Intelligence, ICAI 2008 and Proceedings of the 2008 International Conference on Machine Learning; Models, Technologies and Applications

Volumn , Issue , 2008, Pages 821-827

  Byeon, Boseon ^a   Rasheed, Khaled ^a   Doshi, Prashant ^a  

^a University of Georgia ^*  (United States)

Author keywords

Genetic algorithm; Noise detection; Outlier detection; Prototype selection

Indexed keywords

BINARY CLASSIFICATIONS; CLASSIFICATION ACCURACIES; DEPENDENT VARIABLES; ENHANCED TRAININGS; NOISE DETECTION; NOVEL TECHNIQUES; OUTLIER DETECTION; PROTOTYPE SELECTION; TRAINING DATA SETS; TRAINING DATUM; BINARY CLASSIFICATION; CLASSIFICATION ACCURACY; ENHANCED TRAINING; TRAINING DATA;

ARTIFICIAL INTELLIGENCE; ROBOT LEARNING; GENETIC ALGORITHMS; LEARNING SYSTEMS;

GENETIC ALGORITHMS; CLASSIFICATION (OF INFORMATION);

EID: 62749127269     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. Aggarwal, C. C., and Yu, P., Outlier detection for high dimensional data. In Proceedings of the ACM SIGMOD International Conference on Management of Data, 2001.
2. Arning, A., Agrawal, R., and Raghavan, P., A linear method for deviation detection in large databases, in: Proc. 1996 Int. Conf. Data Mining and Knowledge Discovery (KDD'96), Portland, OR, pp. 164-169, 1996.
3. Brighton, H., and Mellish, C., Advances in Instance Selection for Instance-Based Learning Algorithms, Data Mining and Knowledge Discovery, vol. 6, no. 2, pp. 153-172, 2002.
4. Brodley, C.E., and Friedl, M.A., Identifying and Eliminating Mislabeled Training Instances. In Proc. of the 30th, National Conference on Artificial Intelligence (pp. 799-805), Portland, OR: AAI Press, 1996.
5. Goldberg, D., Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, Mass., 1989.
6. Hodge, V., and Austin, I, A survey of outlier detection methodologies. Artif. Intell. Rev. 22(2), 85-126, 2004.
7. John, G. H., Robust Decision Tree: Removing Outliers from Databases, Proc. First Int'l Conf. Knowledge Discovery and Data Mining, pp. 174-179, Aug. 1995.
8. Michalewicz, Z., Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, New York, 1996.
9. Skalak, D.B., Prototype and feature selection by sampling and random mutation hill climbing algorithms. In Proc. ML-94, Morgan Kaufmann, 1994.
10. Witten, I. and Frank, E., Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufman, second edition, 2005.
11. Xiong, H., Pandey, G., Steinbach, M., and Kumar, V., Enhancing Data Analysis with Noise Removal, IEEE Transactions on Knowledge and Data Engineering (TKDE), 18(3), pp. 304-319, March, 2006.
12. Zhang, T., Ramakrishnan, R., and Livny, M., BIRCH: An Efficient Data Clustering Method for Very Large Databases, Proc. Conf. Management of Data (ACM SIGMOD '96), pp. 103-114, 1996.
13. Zhu, X., and Wu, X., Class noise vs. attribute noise: A quantitative study. Artificial Intelligence Review 22(3) 177-210, 2004.