Preprocessing input data for machine learning by FCA

CEUR Workshop Proceedings

Volumn 672, Issue , 2010, Pages 187-198

  Outrata, Jan ^a  

^a PALACKÝ UNIVERSITY   (Czech Republic)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK DATASETS; COMPARISON OF PERFORMANCE; DECISION TREE INDUCTION; EXPERIMENTAL EVALUATION; FORMAL CONCEPTS; INPUT DATAS; PRE-PROCESSING METHOD;

BENCHMARKING; DATA MINING; DECISION TREES; FORMAL CONCEPT ANALYSIS; LEARNING SYSTEMS;

INPUT OUTPUT PROGRAMS;

EID: 84889599615     PISSN: 16130073     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Belohlavek R., Vychodil V.: Discovery of optimal factors in binary data via a novel method of matrix decomposition. J. Comput. System Sci 76(1)(2010), 3-20.
2. Breiman L., Friedman J. H., Olshen R., Stone C. J.: Classification and Regression Trees. Chapman & Hall, NY, 1984.
3. Fu H., Fu H., Njiwoua P., Mephu Nguifo E.: A comparative study of FCA-based supervised classification algorithms. In: Proc. ICFCA 2004, LNAI 2961, 2004, pp. 313-320.
4. Ganter B., Wille R.: Formal Concept Analysis. Mathematical Foundations. Springer, Berlin, 1999.
5. Kim K. H.: Boolean Matrix Theory and Applications. M. Dekker, 1982.
6. Kuznetsov S. O.: Machine learning and formal concept analysis. In: Proc. ICFCA 2004, LNAI 2961, 2004, pp. 287-312.
7. Michalski R. S.: A theory and methodology of inductive learning. Artificial Intelligence 20(1983), 111-116.
8. Missaoui R., Kwuida L.: What Can Formal Concept Analysis Do for Data Warehouses? In Proc. ICFCA 2009, LNAI 5548, 2009, 58-65.
9. Murphy P. M., Pazzani M. J.: ID2-of-3: constructive induction of M-of-N concepts for discriminators in decision trees. In Proc. of the Eight Int.Workshop on Machine Learning, 1991, 183-187.
10. Murthy S. K., Kasif S., Salzberg S.: A system for induction of oblique decision trees. J. of Artificial Intelligence Research 2(1994), 1-33.
11. Pagallo G., Haussler D.: Boolean feature discovery in empirical learning. Machine Learning 5(1)(1990), 71-100.
12. Piramuthu S., Sikora R. T.: Iterative feature construction for improving inductive learning algorithms. Expert Systems with Applications 36(2, part 2)(2009), 3401-3406.
13. Quinlan J. R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
14. Tan P.-N., Steinbach M., Kumar V.: Introduction to Data Mining. Addison Wesley, Boston, MA, 2006.
15. Valtchev P., Missaoui R., Godin R.: Formal concept analysis for knowledge discovery and data mining: The new challenges. In: Proc. ICFCA 2004, LNAI 2961, 2004, pp. 352-371.