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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-0904e93e-41d2-44a1-8a5f-6f7068ad662d

Czasopismo

Bio-Algorithms and Med-Systems

Tytuł artykułu

Computer-aided analysis of data from evaluation sheets of subjects with autism spectrum disorders

Autorzy Pancerz, K.  Derkacz, A.  Mich, O.  Gomula, J. 
Treść / Zawartość http://medicluster.cm-uj.krakow.pl/pl/czasopismo/
Warianty tytułu
Języki publikacji EN
Abstrakty
EN In this paper, we deal with the problem of the initial analysis of data from evaluation sheets of subjects with autism spectrum disorders (ASDs). In the research, we use an original evaluation sheet including questions about competencies grouped into 17 spheres. An initial analysis is focused on the data preprocessing step including the filtration of cases based on consistency factors. This approach enables us to obtain simpler classifiers in terms of their size (a number of nodes and leaves in decision trees and a number of classification rules).
Słowa kluczowe
EN autism spectrum disorders   classification rules   classification trees   preprocessing  
Wydawca De Gruyter
Czasopismo Bio-Algorithms and Med-Systems
Rocznik 2016
Tom Vol. 12, no. 3
Strony 109--116
Opis fizyczny Bibliogr. 17 poz., rys., tab., wykr.
Twórcy
autor Pancerz, K.
  • Faculty of Mathematics and Natural Sciences, University of Rzeszow, Prof. S. Pigonia Str. 1, 35-310 Rzeszow, Poland, kpancerz@ur.edu.pl
autor Derkacz, A.
  • University of Management and Administration, Zamosc, Poland
autor Mich, O.
  • University of Management and Administration, Zamosc, Poland
autor Gomula, J.
  • Cardinal Stefan Wyszynski University, Warsaw, Poland
Bibliografia
1. Greenes RA. Clinical decision support. The road ahead. Boston, MA: Elsevier, 2007.
2. Garcia S, Luengo J, Herrera F. Data preprocessing in data mining. Intelligent systems reference library, vol. 72. Switzerland: Springer International Publishing, 2015.
3. Han J, Kamber M, Pei J. Data mining: concept and techniques. Waltham, MA: Morgan Kaufmann, 2012.
4. Cios K, Pedrycz W, Swiniarski R, Kurgan L. Data mining. A knowledge discovery approach. New York: Springer, 2007.
5. Grochowski M, Jankowski N. Comparison of instances selection algorithms I. Algorithms survey. In: Rutkowski L, Siekmann J, Tadeusiewicz R, Zadeh LA, editors. Artificial intelligence and soft computing. ICAISC 2004. Ser. LNAI 3070. Berlin/Heidelberg: Springer-Verlag, 2004:598–603.
6. Pancerz K, Derkacz A, Gomuła J. Consistency-based preprocessing for classification of data coming from evaluation sheets of subjects with ASDs. In: Position papers of the 2015 Federated Conference on Computer Science and Information Systems, 13–16 September 2015, Lodz, Poland, 2015:63–7.
7. Pancerz K. Extensions of information systems: the rough set perspective. Trans Rough Sets 2009;X:157–68.
8. Piątek Ł, Pancerz K, Owsiany G. Validation of data categorization using extensions of information systems: experiments on melanocytic skin lesion data. In: Federated Conference on Computer Science and Information Systems, 18–21 September 2011, Szczecin, Poland, 2011:147–51.
9. Pawlak Z. Rough sets. Theoretical aspects of reasoning about data. Dordrecht: Kluwer Academic Publishers, 1991.
10. Pancerz K. On selected functionality of the Classification and Prediction Software System (CLAPSS). In: International Conference on Information and Digital Technologies, 7–9 July 2015, Zilina, Slovakia, 2015:267–74.
11. Pawlak Z, Skowron A. Rudiments of rough sets. Inf Sci 2007;177:3–27.
12. Suraj Z, Pancerz K, Owsiany G. On consistent and partially consistent extensions of information systems. In: Ślęzak D, Wang G, Szczuka M, Duntsch I, Yao Y, editors. Rough sets, fuzzy sets, data mining, and granular computing. Ser. LNAI 3641. Berlin/ Heidelberg: Springer-Verlag, 2005:224–33.
13. Moshkov M, Skowron A, Suraj Z. On testing membership to maximal consistent extensions of information systems. In: Greco S, Hata Y, Hirano S, Inuiguchi M, Miyamoto S, Nguyen HS, Slowinski R, editors. Rough sets and current trends in computing. Ser. LNAI 4259. Berlin/Heidelberg: Springer-Verlag, 2006:85–90.
14. Suraj Z. Some remarks on extensions and restrictions of information systems. In: Ziarko W, Yao Y, editors. Rough sets and current trends in computing. Ser. LNAI 2005. Berlin/Heidelberg: Springer-Verlag, 2001:204–11.
15. Demsar J, Curk T, Erjavec A, Gorup C, Hocevar T, Milutinovic M, et al. Orange: data mining toolbox in Python. J Mach Learn Res 2013;14:2349–53.
16. Bazan JG, Szczuka MS. The rough set exploration system. In: Transactions on rough sets III. Ser. LNAI 3400. Berlin/Heidelberg: Springer-Verlag, 2005:37–56.
17. Grzymala-Busse J. A new version of the rule induction system LERS. Fundam Inf 1997;31:27–39.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-0904e93e-41d2-44a1-8a5f-6f7068ad662d
Identyfikatory
DOI 10.1515/bams-2016-0011