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A Classifier Based on a Decision Tree with Temporal Cuts

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A new method of decision tree construction from temporal data is proposed in the paper. This method uses the so-called temporal cuts for binary partition of data in tree nodes. The novelty of the proposed approach is that the quality of cuts is calculated not on the basis of the discernibility of objects (related to time points), but on the basis of the discernibility of time windows labeled with different decision classes. The paper includes results of experiments performed on our data sets and collections from machine learning repositories. In order to evaluate the presented method, we compared its performance with the classification results of a local discretization decision tree, and other methods well known from literature. Our new method outperforms these known methods.
Wydawca
Rocznik
Strony
263--281
Opis fizyczny
Bibliogr. 28 poz., rys., tab., wykr.
Twórcy
autor
  • Interdisciplinary Centre for Computational Modelling, University of Rzeszów, Pigonia 1, 35-310 Rzeszów, Poland
autor
  • Interdisciplinary Centre for Computational Modelling, University of Rzeszów, Pigonia 1, 35-310 Rzeszów, Poland
  • Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447 Warsaw, Poland
  • Physical Education Faculty, University of Rzeszów, Towarnickiego 3, 35-959 Rzeszów, Poland
autor
  • Physical Education Faculty, University of Rzeszów, Towarnickiego 3, 35-959 Rzeszów, Poland
  • Physical Education Faculty, University of Rzeszów, Towarnickiego 3, 35-959 Rzeszów, Poland
  • Physical Education Faculty, University of Rzeszów, Towarnickiego 3, 35-959 Rzeszów, Poland
Bibliografia
  • [1] AMTI: Home Page, https://www.amti.biz.
  • [2] Battacharrayya GK, Johnson RA. Statistical concepts and methods, Wiley, New York (1977) ISBN-10:0471072044, 13:978-0471072041.
  • [3] Bazan JG, Szczuka M. The rough set exploration system. In: Transactions on Rough Sets III, Springer 2005 pp. 37-56. doi:10.1007/11427834_2.
  • [4] Bazan JG. Hierarchical classifiers for complex spatio-temporal concepts. In: Transactions on Rough Sets IX, Springer 2008 pp. 474-750. doi:10.1007/978-3-540-89876-4_26.
  • [5] Bazan JG, Bazan-Socha S, Buregwa-Czuma S, Pardel PW, Sokolowska B. Predicting the presence of serious coronary artery disease based on 24 hour Holter ECG monitoring. In: M. Ganzha, L. Maciaszek, M. Paprzycki (eds.), Proceedings of the Federated Conference on Computer Science and Information Systems, IEEE Xplore - digital library 2012, pp. 279-286, doi:10.1007/978-3-662-47815-8_7.
  • [6] Bazan JG., Buregwa-Czuma S., Jankowski A.: A Domain Knowledge as A Tool For Improving Classifiers, Fundamenta Informaticae 127 (1-4) (2013) 495-511.
  • [7] Bazan JG, Bazan-Socha S, and Buregwa-Czuma S, Dydo L, Rzasa W, Skowron A. A classifier based on a decision tree with verifying cuts. Fundamenta Informaticae, 2016;143(1-2)1-18. doi:10.3233/FI-2016-1300.
  • [8] Bazan JG, Szpyrka M, Szczur A, Dydo L, Wojtowicz H. Classifiers for Behavioral Patterns Identification Induced from Huge Temporal Data. Fundamenta Informaticae, 2016;143(1-2)19-34. doi:10.3233/FI-2016-1301.
  • [9] Bertsimas D, Dunn J, Pawlowski C, Zhuo YD. Robust Classification. INFORMS Journal on Optimization 2018. URL https://doi.org/10.1287/ijoo.2018.0001.
  • [10] Buregwa-Czuma S, Bazan JG, Bazan-Socha S, Rzasa W, Dydo L, Skowron A. Resolving the Conflicts Between Cuts in a Decision Tree with Verifying Cuts. In Proceedings of IJCRS 2017, Olsztyn 3-7 July, Lecture Notes in Computer Science (LNCS), 10314, Springer, 2017 pp. 403-422 (The Best Application Paper Award). doi:10.1007/978-3-319-60840-2_30.
  • [11] Cherfi A, Nouira K, Ferchichi A. Very Fast C4.5 Decision Tree Algorithm, Applied Artificial Intelligence, Vol. 32, Issue 2, 2018 pp. 119-137. URL https://doi.org/10.1080/08839514.2018.1447479.
  • [12] Diggle PJ. Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Chapman and Hall/CRC 2013. ISBN-9781466560239.
  • [13] Emaad A, Manzoor HL, Leman A. Extremely Fast Decision Tree. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2018). 2018 pp. 1953-1962.
  • [14] Frank E, Hall M, Witten I. The weka workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques", 4th edn. Morgan Kaufman, Burlington 2016.
  • [15] Gama J, Medas P, Rodrigues P. Concept Drift in Decision Trees Learning from Data Streams, Proceedings of the Fourth European Symposium on Intelligent Technologies and their implementation on Smart Adaptive Systems, 2014 pp. 218-225.
  • [16] Kumar KG, Pulabaigari V, Rao AA. Ensemble of randomized soft decision trees for robust classification. Sadhana, 2016;41(3):273-282. doi:10.1007/s12046-016-0465-z.
  • [17] Lichman M. UCI Machine Learning Repository, URL http://archive.ics.uci.edu/ml.
  • [18] Mitsa T. Temporal Data Mining, Chapman and Hall/CRC 2010. ISBN-9781420089769.
  • [19] Nguyen HS. Approximate boolean reasoning: Foundations and applications in data mining. LNCS Transactions on Rough Sets V 4100, 2006 pp. 334-506. doi:10.1007/11847465_16.
  • [20] Pancerz, K., Lewicki, A.: Encoding symbolic features in simple decision systems over ontological graphs for PSO and neural network based classifiers. Neurocomputing, Vol. 144, Elsevier, 2014, pp. 338-345.
  • [21] Pancerz, K., Lewicki, A., Tadeusiewicz, R.: Ant-Based Extraction of Rules in Simple Decision Systems over Ontological Graphs. International Journal of Applied Mathematics and Computer Science, Vol. 25 (2), 2015, pp. 377-387.
  • [22] Pancerz, K.: Paradigmatic and Syntagmatic Relations in Information Systems over Ontological Graphs. Fundamenta Informaticae, Vol. 148 (1-2), IOS Press, Amsterdam, 2016, pp. 229-242.
  • [23] Pawlak Z, Skowron A. Rudiments of rough sets. Information Sciences 2007;177(1):3-27. URL https://doi.org/10.1016/j.ins.2006.06.003.
  • [24] Roddick JF, Hornsby K (Eds.). Temporal, Spatial, and Spatio-Temporal Data Mining, Springer-Verlag Berlin Heidelberg 2001. doi:10.1007/3-540-45244-3.
  • [25] Synak P. Temporal Aspects of Data Analysis: A Rough Set Approach. Ph.D. thesis, The Institute of Computer Science of the Polish Academy of Sciences, Warsaw, Poland, 2003 (In Polish).
  • [26] RSES: Rough Set Exploration System - homepage, URL http://logic.mimuw.edu.pl/rses.
  • [27] WEKA: Data Mining Software in Java - homepage, URL https://www.cs.waikato.ac.nz/ml/weka.
  • [28] Yang Y. Temporal Data Mining via Unsupervised Ensemble Learning, Elsevier (2016). ISBN-13:978-0128116548, 10:0128116544.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4bec00be-3638-446e-b0ee-45fa36e522a5
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