A genetic-based feature construction method for data summarisation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5139 LNAI, Issue , 2008, Pages 39-50

  Alfred, Rayner ^a  

^a UNIVERSITI MALAYSIA SABAH   (Malaysia)

Author keywords

Clustering; Data Summarisation; Feature Construction; Genetic Algorithm

Indexed keywords

CLUSTERING ALGORITHMS; GENETIC ALGORITHMS; NEONATAL MONITORING;

CLUSTERING; DATA SUMMARISATION; DYNAMIC AGGREGATION; FEATURE CONSTRUCTION; FITNESS FUNCTIONS; GENETIC-BASED ALGORITHMS; INPUT DATAS; SCORING MEASURES;

DATA MINING;

EID: 68749115161     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-88192-6_6     Document Type: Conference Paper

References (24)

1. Alfred, R., Kazakov, D.: Data Summarisation Approach to Relational Domain Learning Based on Frequent Pattern to Support the Development of Decision Making. In: 2nd ADMA International Conference, pp. 889-898 (2006)
2. Blockeel, H., Dehaspe, L.: Tilde and Warmr User Manual (1999), http://www.cs.kuleuvan.ac.be/~ml/PS/TWuser.ps.gz
3. Lavrač, N., Flach, P.A.: An extended transformation approach to Inductive Logic Programming. ACM Trans. Comput. Log. 2(4), 458-494 (2001)
4. Salton, G., Wong, A., Yang, C.S.: A Vector Space Model for Automatic Indexing. Commun. ACM 18(11), 613-620 (1975)
5. Pagallo, G., Haussler, D.: Boolean Feature Discovery in Empirical Learning. Machine Learning 5, 71-99 (1990)
6. Hu, Y.J., Kibler, D.F.: Generation of Attributes for Learning Algorithms. In: AAAI/IAAI, vol. 1, pp. 806-811 (1996)
7. Hu, Y.J.: A genetic programming approach to constructive induction. In: Proc. of the Third Annual Genetic Programming Conference, pp. 146-157. Morgan Kauff-man, Madison (1998)
8. Otero, F.E.B., Silva, 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)
9. Bensusan, H., Kuscu, I.: Constructive Induction using Genetic Programming. In: ICML 1996, Evolutionary computing and Machine Learning Workshop (1996)
10. Zheng, Z.: Constructing X-of-N Attributes for Decision Tree Learning. Machine Learning 40(1), 35-75 (2000)
11. Zheng, Z.: Effects of Different Types of New Attribute on Constructive Induction. In: ICTAI, pp. 254-257 (1996)
12. Quinlan, R.J.: Decision-Tree. In: C4.5: Programs for Machine Learning. Morgan Kaufmann Series in Machine Learning (1993)
13. Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)
14. Amaldi, E., Kann, V.: On the Approximability of Minimising Nonzero Variables or Unsatisfied Relations in Linear Systems. Theory Computer Science 209(1-2), 237-260 (1998)
15. Freitas, A.A.: Understanding the Crucial Role of Attribute Interaction in Data Mining. Artif. Intell. Rev. 16(3), 177-199 (2001)
16. Shafti, L.S., Pérez, E.: Genetic Approach to Constructive Induction Based on Non-algebraic Feature Representation. In: R. Berthold, M., Lenz, H.-J., Bradley, E., Kruse, R., Borgelt, C. (eds.) IDA 2003. LNCS, vol. 2810, pp. 599-610. Springer, Heidelberg (2003)
17. Vafaie, H., DeJong, K.: Feature Space Transformation Using Genetic Algorithms. IEEE Intelligent Systems 13(2), 57-65 (1998)
18. Koza, J.R.: Genetic Programming: On the programming of computers by means of natural selection. Statistics and Computing 4(2) (1994)
19. Krawiec, K.: Genetic Programming-based Construction of Features for Machine Learning and Knowledge Discovery Tasks. Genetic Programming and Evolvable Machines 3, 329-343 (2002)
20. Davies, D.L., Bouldin, D.W.: A Cluster Separation Measure. IEEE Trans. Pattern Analysis and Machine Intelligence 1, 224-227 (1979)
21. Shannon, C.E.: A mathematical theory of communication. Bell system technical journal 27 (1948)
22. Wiener, N.: Cybernetics: Or Control and Communication in Animal and the Machine. MIT Press, Cambridge (2000)
23. Srinivasan, A., Muggleton, S., Sternberg, M.J.E., King, R.D.: Theories for Muta-genicity: A Study in First-Order and Feature-Based Induction. Artif. Intell. 85(1-2), 277-299 (1996)
24. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (1999)