About One Algorithm For Generating Reference Data In Pattern Recognition
Keywords:
Abstract
In this paper the issues like preprocessing of medical data, reclassification of the training sets and determining the importance of classes, formation of reference tables, selection of an informative features set that differentiate between class objects, formed by medical professionals are discussed and solved. Mainly in the most studied references [5-8, 11-13] the Fisher's criterion is used to obtain solutions to problems/tasks. Also for solving problems, the algorithms for an estimate calculation as well as the related software programs are used. For all cases, algorithms and software programs are suggested.
The study consists of two important steps. The first step is to build a reference table, based on the importance of the features and objects as well as their contribution to the classes [1-4, 9, 10]; the second step is concerned with the choice of the most useful characteristic features set to be investigated. This corresponds to solving the issue of selection of set of informative features from a given table, their visualization, and the determination of the contribution of the features set to the formation of classes [1-13].
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References
Zhuravlev Yu.I. “Selected scientific works”. – M: “Magistr” Publishing house, 1998. - 420 p.
Yu, L., H. Liu., “Efficient Feature Selection via Analysis of Relevance and Redundancy”. – J. Mach. Learn. Res., Vol. 5, 2004, No Oct, pp. 1205-1224.
Yu, L.: “Toward Integrating Feature Selection Algorithm for Classification and Clustering”, IEEE Transaction on Knowledge and Data Engineering 17(4), 491-502
Yan, K., D. Zhang. “Feature Selection and Analysis on Correlated Gas Sensor Data with Recursive Feature Elimination”, – Sensors Actuators, B Chem., Vol. 212, Jun 2015, pp. 353-363.
Nishanov A.Kh,, Turakulov Kh.A., Turakhanov Kh.V. “A decision rule for identification of eye pathologies”, Biomedical Engineering (1999) 33(4) 178-179.
Nishanov A. Kh,,Turakulov Kh.A.,Turakhanov Kh.V. “A decisive rule in classifying diseases of the visual system” Meditsinskaia tekhnika (1999) (4) 16-18.
Xiang Fang, Lina Wang. “Feature Selection Based on Fisher Criterion and Sequential Forward Selection for Intrusion Detection”, Revista de la Facultad de Ingeniería U.C.V., Vol. 32, N° 1, pp.498-503, 2017.
Linhui Sun, Sheng Fu and Fu Wang. “Decision tree SVM model with Fisher feature selection for speech emotion recognition”, Sun et al. EURASIP Journal on Audio, Speech, and Music Processing (2019) 2019:2.
https://doi.org/10.1186/s13636-018-0145-5
Yan, X.,Tan, M., Yan,Y., Lü, M. (2012). “Research on Hidden Markov Model-Based And Neural Network-Based Intrusion Detections”, Computer Applications and Software. 29(2), 294–297.
Jing, X.,Wang, H., Nie, K., Luo, Z. (2012). “Feature Selection Algorithm Based on IMGA and MKSVM to Intrusion Detection”, Computer Science, 39(7), 96–100.
Koushal Kumar, Jaspreet Singh Batth. “Network Intrusion Detection with Feature Selection Techniques using Machine-Learning Algorithms”, International Journal of Computer Applications (0975 – 8887) Volume 150 – No.12, September 2016, 1-13.
Nishanov А.Kh., Djurayev G.P., Kasanova М.Kh. “Improved algorithms for calculating evaluations in processing medical data”, National Institute of Science Communication and Information Resources (NISCAIR)-India, 2019,-3158-3165.
Kamilov M., Nishanov A., Beglerbekov R. “Modified stages of algorithms for computing estimates in the space of informative features”, International Journal of Innovative Technology and Exploring Engineering (2019) 8(6).
Sulaiman M Labadin J. “Feature selection based on mutual information”, Publisher: Institute of Electrical and Electronics Engineers (IEEE), 2015 pp: 1-6.
Xuelian Deng, Yuqing Li, Jian Weng, Jilian Zhang. “Feature selection for text classification: A review”, Multimedia Tools and Applications, 2019, 1007/s11042-018-6083-5.
Ma, S. and Huang, J. (2008) “Penalized feature selection and classification in bioinformatics”, Briefings in Bioinformatics. doi: 10.1093/bib/bbn027.
Sun, L., Fu, S. and Wang, F. (2019) “Decision tree SVM model with Fisher feature selection for speech emotion recognition”, Eurasip Journal on Audio, Speech, and Music Processing, 2019(1). doi: 10.1186/s13636-018-0145-5.
Krishna, R. S. B. and Aramudhan, M. (2014) “Feature selection based on information theory for pattern classification”, in 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies, ICCICCT 2014. doi: 10.1109 / ICCICCT. 2014. 6993149.
Mokshin, V.V. et al. (2018) “Parallel genetic algorithm of feature selection for complex system analysis”, in Journal of Physics: Conference Series. doi: 10.1088/1742-6596/1096/1/ 012089.
Solorio-Fernández, S., Carrasco-Ochoa, J.A. and Martínez-Trinidad, J.F. (2019) “A review of unsupervised feature selection methods”, Artificial Intelligence Review.doi: 10.1007/s10462-019-09682-y.
Urbanowicz, R. J. et al. (2018) “Benchmarking relief-based feature selection methods for bioinformatics data mining”, Journal of Biomedical Informatics, 85. doi: 10.1016/j.jbi.2018.07.015.
Cai, J. et al. (2018) “Feature selection in machine learning: A new perspective”, Neurocomputing, 300. doi: 10.1016/j.neucom.2017.11.077.
Ghaemi, M. and Feizi-Derakhshi, M. R. (2016) “Feature selection using Forest Optimization Algorithm”, Pattern Recognition. Elsevier Ltd, 60, pp. 121–129. doi: 10.1016/j.patcog.2016.05.012.
