DARA: Data summarisation with feature construction

Proceedings - 2nd Asia International Conference on Modelling and Simulation, AMS 2008

Volumn , Issue , 2008, Pages 830-835

  Alfred, Rayner ^a  

^a UNIVERSITI MALAYSIA SABAH   (Malaysia)

Author keywords

[No Author keywords available]

Indexed keywords

AGGLOMERATION; ALPHA PARTICLE SPECTROMETERS; ASSET MANAGEMENT; CHLORINE COMPOUNDS; PARTICLE SPECTROMETERS;

CARDINALITY; DYNAMIC AGGREGATION; FEATURE CONSTRUCTION; FREQUENCY MATRIX; INTERNATIONAL CONFERENCES; LEARNING DATA; MODELLING AND SIMULATION; PAPER ADDRESSES; PREDICTIVE ACCURACY;

NEONATAL MONITORING;

EID: 50249088041     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AMS.2008.131     Document Type: Conference Paper

References (24)

1. D. W. Aha. Incremental constructive induction: An instance-based approach. In ML, pages 117-121, 1991.
2. R. Alfred and D. Kazakov. Data summarization approach to relational domain learning based on frequent pattern to support the development of decision making. In ADMA, pages 889-898, 2006.
3. R. Alfred and D. Kazakov. Pattern-based transformation approach to relational domain learning using dynamic aggregation for relational attributes. In DMIN, pages 118-124, 2006.
4. R. Alfred and D. Kazakov. Discretization numbers for multiple-instances problem in relational database. In ADBIS, pages 55-65, 2007.
5. E. Amaldi and V. Kann. On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems. Theor. Comput. Sci., 209(1-2):237-260, 1998.
6. H. Bensusan and I. Kuscu. Constructive induction using genetic programming. In T. Fogarty and G. Venturini, editors, (ICML) '96, Evolutionary computing and Machine Learning Workshop, 1996.
7. D. Davies and D. Bouldin. A cluster separation measure. pages 224-227, 1979.
8. T. G. Dietterich and R. S. Michalski. Inductive learning of structural descriptions: Evaluation criteria and comparative review of selected methods. Artif. Intell., 16(3):257-294, 1981.
9. A. A. Freitas. Understanding the crucial role of attribute interaction in data mining. Artif. Intell. Rev., 16(3): 177-199, 2001.
10. H. J. Adaptation in Natural and Artificial Systems. University of Michigan Press, 1975.
11. M. Kirsten and S. Wrobel. Relational distance-based clustering. In ILP, pages 261-270, 1998.
12. J. R. Koza. Genetic programming: On the programming of computers by means of natural selection. Statistics and Computing, 4(2), 1994.
13. Z. Michalewicz. Genetic Algorithms Plus Data Structures Equals Evolution Programs. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 1994.
14. G. Pagallo and D. Haussler. Boolean feature discovery in empirical learning. Machine Learning, 5:71-99, 1990.
15. R. J. Quinlan. C4.5: Programs for Machine Learning (Morgan Kaufmann Series in Machine Learning). Morgan Kaufmann, January 1993.
16. L. S. Shafti and E. Pérez. Genetic approach to constructive induction based on non-algebraic feature representation. In IDA, pages 599-610, 2003.
17. C. E. Shannon. A mathematical theory of communication. Bell system technical journal, 27, 1948.
18. A. Srinivasan, S. Muggleton, M. J. E. Sternberg, and R. D. King. Theories for mutagenicity: A study in first-order and feature-based induction. Artif. Intell., 85(1-2):277-299, 1996.
19. H. Vafaie and K. DeJong. Feature space transformation using genetic algorithms. IEEE Intelligent Systems, 13(2):57-65, 1998.
20. N. Wiener. Cybernetics: Or Control and Communication in Animal and the Machine. MIT Press, Cambridge, MA, USA, 2000.
21. I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, 1999.
22. H. Y. A genetic programming approach to constructive induction. In In Proc. of the Third Annual Genetic Programming Conference, pages 146-157. Morgan Kauffman, Madison, Wisconsin, 1998.
23. Z. Zheng. Effects of different types of new attribute on constructive induction. In ICTAI, pages 254-257, 1996.
24. Z. Zheng. Constructing x-of-n attributes for decision tree learning. Machine Learning, 40(1):35-75, 2000.