Information preserving multi-objective feature selection for unsupervised learning

GECCO 2006 - Genetic and Evolutionary Computation Conference

Volumn 2, Issue , 2006, Pages 1545-1552

  Mierswa, Ingo ^a   Wurst, Michael ^a  

^a TU DORTMUND UNIVERSITY   (Germany)

Author keywords

Multi objective feature selection; Pareto front segmentation; Unsupervised learning

Indexed keywords

DATA STRUCTURES; FEATURE EXTRACTION; INFORMATION ANALYSIS; OPTIMIZATION; PROBLEM SOLVING; SET THEORY;

MULTI-OBJECTIVE FEATURE SELECTION; PARETO FRONT SEGMENTATION; PROTOTYPICAL POINTS; UNSUPERVISED LEARNING;

LEARNING SYSTEMS;

EID: 33750237835     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1143997.1144248     Document Type: Conference Paper

References (23)

1. A. Ben-Hur, D. Horn, H. T. Siegelmann, and V. Vapnik. Support vector clustering. Journal of Machine Learning Research, 2001.
2. G. A. Coello Coello. A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowledge and Information Systems, 1(3):129-156, 1999.
3. T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein. Introduction to Algorithms. The MIT Press, 2001.
4. D. Davies and D. Bouldin. A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2):224-227, 1979.
5. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multi-objective genetic algorithm: Nsga-ii. Technical report. Kanpur Genetic Algorithms Laboratory. Indian Institute of Technology, 2002.
6. A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, Series B, 39(1):1-38, 1977.
7. C. Emmanouilidis, A. Hunter, and J. MacIntyre. A multiobjective evolutionary setting for feature selection and a commonality-based crossover operator. In Proc. of the Congress on Evolutionary Computation (CEC), pages 309-316, 2000.
8. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. of the International Conference on Knowledge Discorvery in Databases (KDD), pages 226-231, 1996.
9. S. Fischer, R. Klinkenberg, I. Mierswa, and O. Ritthoff. Yale: Yet Another Learning Environment - Tutorial. Technical Report CI-136/02. Collaborative Research Center 531. University of Dortmund. Dortmund. Germany, 2002.
10. R. Fisher. The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2):179-188, 1936.
11. J. A. Hartigan and M. A. Wong. A k-means clustering algorithm. Applied Statistics, 28(1):100-108, 1979.
12. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer series in statistics. Springer, 2001.
13. A. Jain, M. Murty, and P. Flynn. Data clustering: a review. ACM Computing Surveys, 3(31):264-323, 1999.
14. Y. Kim, W. Street, and F. Menczer. Feature selection in unsupervised learning via evolutionary search. In Proc. of the 6th ACM SIGKDD international conference on Knowledge discovery and data mining. pages 365-369, New York, NY, USA, 2000. ACM Press.
15. Y. Kim, W. N. Street, and F. Menczer. Evolutionary model selection in unsupervised learning. Intelligent Data Analysis, 6:531-556, 2002.
16. M. Morita, R. Sabourin, F. Bortolozzi, and C. Suen. Unsupervised feature selection using multi-objective genetic algorithms for handwritten word recognition. In Proc. of the 7th International Conference on Document Analysis and Recognition (ICDAR), 2003.
17. F. Murtagh. Clustering in massive data sets, pages 501-543. Kluwer Academic Publishers, 2002.
18. M. Raymer, W. Punch, E. Goodman, L. Kuhn, and A. Jain. Dimensionality reduction using genetic algorithms. IEEE Transactions on Evolutionary Computation, 4, 2000.
19. V. Roth and T. Lange. Feature selection in clustering problems. In Proc. of Neural Information Processing Systems (NIPS), 2003.
20. J. Sander, M. Ester, H.-P. Kriegel, and X. Xu. Density-based clustering in spatial databases: The algorithm gdbscan and its applications. Data Minining and Knowledge Discovery, 2(2):169-194, 1998.
21. W. Wolberg, W. Street, D. Heisey, and O. Mangasarian. Computer-derived nuclear "grade" and breast cancer prognosis. Analytical and Quantitative Cytology and Histology, 17:257-264, 1995.
22. J. Yang and V. Honavar. Feature subset selection using a genetic algorithm. IEEE Intelligent Systems, 13:44-49, 1998.
23. E. Zitzler and L. Thiele. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3(4):257-271, 1999.