Classification of MMPI profiles of patients with mental disorders - Experiments with attribute reduction and extension

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

Volumn 6401 LNAI, Issue , 2010, Pages 411-418

  Gomuła, Jerzy ^a,b   Pancerz, Krzysztof ^c   Szkoła, Jarosław ^c  

^a Medan Foundation   (Poland)

^b CARDINAL STEFAN WYSZY SKI UNIVERSITY   (Poland)

^c UNIVERSITY OF INFORMATION TECHNOLOGY AND MANAGEMENT   (Poland)

Author keywords

attribute extension; attribute reduction; classification; MMPI profiles; rough sets

Indexed keywords

ATTRIBUTE EXTENSION; ATTRIBUTE REDUCTION; CLASSIFICATION; MMPI PROFILES; ROUGH SET;

ROUGH SET THEORY;

EID: 78349239261     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-16248-0_58     Document Type: Conference Paper

References (15)

1. Lachar, D.: The MMPI: Clinical assessment and automated interpretations. Western Psychological Services, Fate Angeles (1974)
2. Gomula, J., Pancerz, K., Szkola, J.: Analysis of MMPI profiles of patients with mental disorders - the first unveil af a new computer tool. In: Proceedings of the XVII International Conference on Systems Science (ICSS 2010), Wroclaw, Poland (2010)
3. Duch, W., Kucharski, T., Gomula, J., Adamczak, R.: Machine learning methods in analysis of psychometric data. Application to Multiphasic Personality Inventory MMPI-WISKAD, Toruń (1999) (in polish)
4. Gomula, J., Paja, W., Pancerz, K., Szkola, J.: A preliminary attempt to rules generation for mental disorders. In: Proceedings of the International Conference on Human System Interaction (HSI 2010), Rzeszow, Poland (2010)
5. Choynowski, M.: Multiphasic Personality Inventory. Psychometry Laboratory. Polish Academy of Sciences, Warsaw (1964) (in polish)
6. Pluzek, Z.: Value of the WISKAD-MMPI test for nosological differential diagnosis. The Catholic University of Lublin (1971) (in polish)
7. Bazan, J.G., Szczuka, M.S.: The Rough Set Exploration System. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 37-56. Springer, Heidelberg (2005)
8. The Rough Set Exploration System, http://logic.mimuw.edu.pl/~rses/
9. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2005)
10. WEKA, http://www.cs.waikato.ac.nz/ml/weka/
11. Bazan, J.G., Nguyen, H.S., Nguyen, S.H., Synak, P., Wroblewski, J.: Rough set algorithms in classification problem. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough Set Methods and Applications. Studies in Fuzziness and Soft Computing, pp. 49-88. Physica-Verlag, Heidelberg (2000)
12. Wroblewski, J.: Genetic algorithms in decomposition and classification problem. In: Rough Sets in Knowledge Discovery, vol. 2, pp. 471-487
13. Wroblewski, J.: Covering with reducts - a fast algorithm for rule generation. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 402-407. Springer, Heidelberg (1998)
14. Grzymala-Busse, J.: A new version of the rule induction system LERS. Fundamenta Informaticae 31, 27-39 (1997)
15. Quinlan, J.: Induction of decision trees. Machine Learning 1(1), 81-106 (1986)