Unsupervised Learning of Mutagenesis Molecules Structure Based on an Evolutionary-Based Features Selection in DARA

  • Rayner Alfred
  • Irwansah Amran
  • Leau Yu Beng
  • Tan Soo Fun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7691)


The importance of selecting relevant features for data modeling has been recognized already in machine learning. This paper discusses the application of an evolutionary-based feature selection method in order to generate input data for unsupervised learning in DARA (Dynamic Aggregation of Relational Attributes). The feature selection process which is based on the evolutionary algorithm is applied in order to improve the descriptive accuracy of the DARA (Dynamic Aggregation of Relational Attributes) algorithm. The DARA algorithm is designed to summarize data stored in the non-target tables by clustering them into groups, where multiple records stored in non-target tables correspond to a single record stored in a target table. This paper addresses the issue of optimizing the feature selection process to select relevant set of features for the DARA algorithm by using an evolutionary algorithm, which includes the evaluation of several scoring measures used as fitness functions to find the best set of relevant features. The results show the unsupervised learning in DARA can be improved by selecting a set of relevant features based on the specified fitness function which includes the measures of the dispersion and purity of the clusters produced.


Feature Selection Data Summarization Clustering Genetic Algorithm Data Reduction 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kramer, S., Lavrac, N., Flach, P.: Propositionalisation Approaches to Relational Data Mining. In: Deroski, S., Lavrac, N. (eds.) Relational Data Mining. Springer (2001)Google Scholar
  2. 2.
    Alfred, R., Kazakov, D.: Data Summarization Approach to Relational Domain Learning Based on Frequent Pattern to Support the Development of Decision Making. In: Li, X., Zaïane, O.R., Li, Z.-h. (eds.) ADMA 2006. LNCS (LNAI), vol. 4093, pp. 889–898. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    van Rijsbergen, C.J.: Information Retrieval. Butterworth (1979)Google Scholar
  4. 4.
    Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill Book Company (1984)Google Scholar
  5. 5.
    Horvath, T., Wrobel, S., Bohnebeck, U.: Relational instance-based learning with lists and terms. Machine Learning 43(1), 53–80 (2001)zbMATHCrossRefGoogle Scholar
  6. 6.
    Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Communications of the ACM 18, 613–620 (1975)zbMATHCrossRefGoogle Scholar
  7. 7.
    Bensusan, H., Kuscu, I.: Constructive Induction using Genetic Programming. In: Fogarty, T., Venturini, G. (eds.) Evolutionary Computing and Machine Learning Workshop, ICML 1996 (1996)Google Scholar
  8. 8.
    Aha, D.W., Bankert, R.L.: Feature Selection for Case-Based Classification of Cloud Types. In: Proceedings of the AAAI 1994 Workshop on Case-Based Reasoning. AAAI Press, Seattle (1994)Google Scholar
  9. 9.
    Devijver, P.A., Kittler, J.V.: Pattern Recognition: A Statistical Approach. Prentice Hall (1982)Google Scholar
  10. 10.
    Doak, J.: An Evaluation of Feature Selection Methods and Their Application to Computer Security. Technical report, UC Davis Department of Computer Science (1992)Google Scholar
  11. 11.
    Skalak, D.B.: Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms. In: ICML, pp. 293–301 (1994)Google Scholar
  12. 12.
    Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)Google Scholar
  13. 13.
    Fernando, E.B., Monique, M.S., Freitas, A.A., Nievola, J.C.: Genetic Programming for Attribute Construction in Data Mining. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 384–393. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  14. 14.
    Kudo, M., Sklansky, J.: Comparison of Algorithms That Select Features for Pattern Classifiers. Pattern Recognition 33(1), 25–41 (2000)CrossRefGoogle Scholar
  15. 15.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company, Inc. (1989)Google Scholar
  16. 16.
    Davies, D.L., Bouldin, D.W.: A Cluster Separation Measure. IEEE Trans. Pattern Analysis and Machine Intelligence, 224–227 (1979)Google Scholar
  17. 17.
    Breiman, L., Friedman, J., Olshen, T., Stone, C.: Classification and Regression Trees. Wadsworth International, California (1984)Google Scholar
  18. 18.
    Raileanu, L.E., Stoffel, K.: Theoretical Comparison Between the Gini Index and Information Gain Criteria. Annals of Mathematics and Artificial Intelligence 41(1), 77–93 (2004)MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
    Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufman (1999)Google Scholar
  20. 20.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, Los Altos (1993)Google Scholar
  21. 21.
    Srinivasan, A., Muggleton, S.H., Sternberg, M.J.E., King, R.D.: Theories for mutagenicity: A Study in first-order and feature-based induction. Artificial Intelligence 85 (1996)Google Scholar
  22. 22.
    Alfred, R.: Feature transformation: A genetic-based feature construction method for data summarization. Computational Intelligence 26(3), 337–357 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  23. 23.
    Alfred, R.: The Study of Dynamic Aggregation of Relational Attributes on Relational Data Mining. In: Alhajj, R., Gao, H., Li, X., Li, J., Zaïane, O.R. (eds.) ADMA 2007. LNCS (LNAI), vol. 4632, pp. 214–226. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Rayner Alfred
    • 1
  • Irwansah Amran
    • 1
  • Leau Yu Beng
    • 1
  • Tan Soo Fun
    • 1
  1. 1.School of Engineering and Information TechnologyUniversiti Malaysia Sabah, Jalan UMSKota KinabaluMalaysia

Personalised recommendations