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References 1. Karabadji N E I, Seridi H, Khelf I, et al. Improved decision tree construction based on attribute selection and data sampling for fault diagnosis in rotating machines. Engineering Applications of Artificial Intelligence, 2014, 35(35):71-83. 2. Zhang Q H, Qin A, Shu L, et al. Vibration sensor based intelligent fault diagnosis system for large machine unit in petrochemical industry. International Journal of Distributed Sensor Networks, 2015, 2015(3):1376-1381. 3. Jin S, Cui W, Jin Z, et al. AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault


The aim of this study was to perform and evaluate the accuracy of classification of grains of different cultivars of malting barley. The grains of eight cultivars: Blask, Bor do, Con chita, Kormoran, Mercada, Serwal, Signora, Victoriana, with three moisture content: 12, 14, 16% were examined. The selected parameters of the surface texture of grain mass obtained from images taken using the techniques of hyperspectral imaging were determined. The accuracy of grains discrimination carried out using different methods of selection and classification of data was compared. The pairwise comparison and comparison of three, four and eight cultivars of malting barley were carried out. The most accurate discrimination was determined in the case of the pairwise comparison. Victoriana cultivar was the most different from the others. The most similar texture of grain mass was found in the comparison of cultivars: Blask and Mercada. In the case of eight examined cultivars of malting barley, the most accurate discrimination (classification error – 55%) was obtained for images taken at the moisture content of 14% and at a wavelength of 750 nm, for the attributes selection performed with the use of probability of error and average correlation coefficient (POE+ACC) method and the discrimination carried out using the linear discriminant analysis (LDA).


Stroke is the third most common cause of death and the most common cause of long-term disability among adults around theworld. Therefore, stroke prediction and diagnosis is a very important issue. Data mining techniques come in handy to help determine the correlations between individual patient characterisation data, that is, extract from the medical information system the knowledge necessary to predict and treat various diseases. The study analysed the data of patients with stroke using eight known classification algorithms (J48 (C4.5), CART, PART, naive Bayes classifier, Random Forest, Supporting Vector Machine and neural networks Multilayer Perceptron), which allowed to build an exploration model given with an accuracy of over 88%. The potential features of patients, which may be factors that increase the risk of stroke, were also indicated.


The present research examines a wide range of attribute selection methods – 86 methods that include both ranking and subset evaluation approaches. The efficacy evaluation of these methods is carried out using bioinformatics data sets provided by the Latvian Biomedical Research and Study Centre. The data sets are intended for diagnostic task purposes and incorporate values of more than 1000 proteomics features as well as diagnosis (specific cancer or healthy) determined by a golden standard method (biopsy and histological analysis). The diagnostic task is solved using classification algorithms FURIA, RIPPER, C4.5, CART, KNN, SVM, FB+ and GARF in the initial and various sets with reduced dimensionality. The research paper finalises with conclusions about the most effective methods of attribute subset selection for classification task in diagnostic proteomics data.

. Thi, L. Huong, V.D. Thi, N.L. Giang. Metric Based Attribute Reduction Method in Dynamic Decision Tables. - Cybernetics and Information Technologies, Vol. 16, 2016, No 2, pp. 3-15. 5. Tsang, E.C.C., D.G. Chen, D.S. Yeung, X.Z. Wang, J.W.T. Lee. Attributes Reduction Using Fuzzy Rough Sets. - IEEE Trans. Fuzzy Syst., Vol. 16, 2008, pp. 1130-1141. 6. Dai, J., Q. Xu. Attribute Selection Based on Information Gain Ratio in Fuzzy Rough Set Theory with Application to Tumor Classification. - Applied Soft Computing, Vol. 13, 2013, pp. 211-221. 7. Hu, Q., D.R. Yu, Z. X. Xie

Information, PubChem Compound Database; January 20, 2011. 9. I. V. Tetko, J. Gasteiger, R. Todeschini, A. Mauri, D. Livingstone, P. Ertl, V. A. Palyulin, E. V. Radchenko, N. S. Zefirov, A. S. Makarenko, V. Y. Tanchuk and V. V. J. Prokopenko, Virtual computational chemistry laboratory - design and description, Comput. Aid. Mol. Des . 19 (2005) 453-463; DOI: 10.1007/s10822-005-8694-y. 10. M. A. Hall and G. Holmes, Benchmarking attribute selection techniques for discrete class data mining, IEEE Trans. Knowl. Data Eng . 15 (2003

). Geomorphometry: concepts, software, applications . Developments in Soil Science, 33. Amsterdam: Elsevier. Hijmans, R.J. (2016). raster: Geographic data analysis and modeling . Huete, A., Didan, K., Miura, T., Rodriguez, E., Gao, X. & Ferreira L.(2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ ., 83(1–2), 195–213. DOI: 10.1016/S0034-4257(02)00096-2. Lecours, V., Devillers, R., Simms, A.E., Lucieer, V.L. & Brown C.J. (2017). Towards a framework for terrain attribute

, H. (2008), “Context-based market basket analysis in a multiple-store environment”, Decision Support Systems, Vol. 45, pp. 150-163. Tiwari, R. and Singh, M.P. (2010), “Correlation-based Attribute Selection using Genetic Algorithm”, International Journal of Computer Application, Vol. 4, No. 8, pp. 28-34. Tomar, N. and Manjhvar, A.K. (2015), “A survey on data mining optimization techniques”, International Journal of Science Technology & Engineering, Vol. 2, No. 6, pp. 130-133. Vinod, H.D. (1964), “Integer programming and the theory of grouping”, Journal of the

. (2009): Formalizing the Logic-Automaton Connection . – In: Proc. of the 22nd International Conference on Theorem Proving in Higher Order Logics, LNCS No 5674, pp.147–163. [22] Shiue Y.R. and Guh R.S. (2006): The optimization of attribute selection in decision tree-based production control systems . – The International Journal of Advanced Manufacturing Technology, vol.28, pp.737-746. [23] Shenoy P.P. (1998): Game Trees For Decision Analysis. – Theory and Decision, vol.44, No.2, April, pp.149-171. [24] Deptuła A. and Partyka M.A. (2013): Discrete optimization of a

minimalizacji indywidualnych cząstkowych wielowartościowych funkcji logicznych, Studia i Monografie Nr 109, Politechnika Opolska - Oficyna Wydawnicza, Opole, 1999. 26. Rudnicki J.: The evaluation of the vibration measurement usability of electronic indicator LEMAG „PREMET C”. Journal of Polish CIMAC, Vol. 7, 2, pp. 167-172, 2012 27. Shwnoy P.P.: Game trees for decision analysis. Theory and decision, 44(2), April, pp.149-171, 1998 28. Shiue Y.R., Guh. R.S.: The optimization of attribute selection in decision tree-based production control systems. The International Journal of