Abstract
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).
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
References
1. Greenes RA. Clinical decision support. The road ahead. Boston, MA: Elsevier, 2007.Search in Google Scholar
2. Garcia S, Luengo J, Herrera F. Data preprocessing in data mining. Intelligent systems reference library, vol. 72. Switzerland: Springer International Publishing, 2015.10.1007/978-3-319-10247-4Search in Google Scholar
3. Han J, Kamber M, Pei J. Data mining: concept and techniques. Waltham, MA: Morgan Kaufmann, 2012.Search in Google Scholar
4. Cios K, Pedrycz W, Swiniarski R, Kurgan L. Data mining. A knowledge discovery approach. New York: Springer, 2007.Search in Google Scholar
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, 2004:598–603.10.1007/978-3-540-24844-6_90Search in Google Scholar
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, 2015:63–7.10.15439/2015F393Search in Google Scholar
7. Pancerz K. Extensions of information systems: the rough set perspective. Trans Rough Sets 2009;X:157–68.10.1007/978-3-642-03281-3_6Search in Google Scholar
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.Search in Google Scholar
9. Pawlak Z. Rough sets. Theoretical aspects of reasoning about data. Dordrecht: Kluwer Academic Publishers, 1991.10.1007/978-94-011-3534-4Search in Google Scholar
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, 2015:267–74.10.1109/DT.2015.7222984Search in Google Scholar
11. Pawlak Z, Skowron A. Rudiments of rough sets. Inf Sci 2007;177:3–27.10.1016/j.ins.2006.06.003Search in Google Scholar
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, 2005:224–33.10.1007/11548669_24Search in Google Scholar
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, 2006:85–90.10.1007/11908029_10Search in Google Scholar
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, 2001:204–11.10.1007/3-540-45554-X_24Search in Google Scholar
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.Search in Google Scholar
16. Bazan JG, Szczuka MS. The rough set exploration system. In: Transactions on rough sets III. Ser. LNAI 3400. Berlin/Heidelberg: Springer-Verlag, 2005:37–56.10.1007/11427834_2Search in Google Scholar
17. Grzymala-Busse J. A new version of the rule induction system LERS. Fundam Inf 1997;31:27–39.10.3233/FI-1997-3113Search in Google Scholar
©2016 Walter de Gruyter GmbH, Berlin/Boston
