ADAPTIVE HUMAN MACHINE INTERACTION APPROACH FOR FEATURE SELECTION-EXTRACTION TASK IN MEDICAL DATA MINING
DOI:
https://doi.org/10.47839/ijc.17.2.997Keywords:
Human Machine Interaction, feature selection, feature extraction, Medical Data Mining, Oja’s neuron, Neural Network Approach.Abstract
Feature Selection task is one of the most complicated and actual in the areas of Data Mining and Human Machine Interaction. Many approaches to its solving are based on non-mathematical and presentative hypothesis. New approach to evaluation of medical features information quantity, based on optimized combination of feature selection and feature extraction methods is proposed. This approach allows us to produce optimal reduced number of features with linguistic interpreting of each of them. Hybrid system of feature selection/extraction based on Neural Network-Physician interaction is investigated. This system is numerically simple, can produce feature selection/extraction with any number of factors in online mode using neural network-physician interaction based on Oja’s neurons for online principal component analysis and calculating distance between first principal component and all input features. A series of experiments confirms efficiency of proposed approaches in Medical Data Mining area and allows physicians to have the most informative features without losing their linguistic interpreting.References
A. Mosenia; S. Sur-Kolay; A. Raghunathan; N.K. Jha, “Wearable medical sensor-based system design: a survey,” IEEE Trans. on Multi-Scale Computing Systems, vol. 3, issue 2, pp. 124-138, 2017.
S.P. Panda, “Automated speech recognition system in advancement of human-computer interaction”, 2017 International Conference on Computing Methodologies and Communication (ICCMC), 2017, pp. 302-306, DOI: 10.1109/ICCMC.2017.8282696.
G. M. Phade, Prerna D. Uddharwar, P. A. Dhulekar, S. T. Gandhe, “Motion estimation for human-machine interaction,” 2014 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2014, pp. 149-154, DOI: 10.1109/ISSPIT.2014.7300579.
J.C. Bezdek, P. Castelaz, “Prototype classification and feature selection with fuzzy sets,” IEEE Trans. on Systems, Man and Cybernetics, vol. 7, no. 2, pp. 87-92, 1977.
S.K. Pal, “Fuzzy set theoretic measures for automatic feature evaluation: II,” Information Sciences, vol. 64, 1992, 165-179.
S.K. Pal, B. Chakraborty, “Fuzzy set theoretic measures for automatic feature evaluation,” IEEE Trans. on Systems, Man and Cybernetics, vol. 16, no. 5, pp. 754-760, 1986.
K.L. Priddy, S.K. Rogers, D.W. Ruck, G.L. Tarr, M. Kabrisky, “Bayesian selection of important features or feedforward neural networks,” Neurocomputing, no. 5, pp. 91-103, 1993.
J.M. Steppe, K.W. Bauer Jr., “Improved feature screening in feedforward neural networks,” Neurocomputing, vol. 13, pp. 47-58, 1996.
R.K. De, N.R. Pal, S.K. Pal, “Feature analysis: neural network and fuzzy set theoretic approaches,” Pattern Recognition, vol. 30,
M. Pregenzer, G. Pfurtscheller, D. Flotzinger, “Automated feature selection with a distinctive sensitive learning vector quantizer,” Neurocomputing, vol. 11, pp. 19-29, 1996.
C. R. Rao, “The use and interpretation of principal component analysis in applied research,” Sankhyā: The Indian Journal of Statistics, Series A, vol. 26, no. 4, pp. 329-358, 1964.
M. Okamoto, “Optimality principal components multivariate analysis,” Proceedings of the 3rd Int. Symp., Dayton, 1967.
M. Okamoto, M. Kanazawa, “Minimization of eigenvalues of a matrix and optimality of principal components,” Ann. Math. Statist, vol. 39, no. 3, pp. 1-20, 1968.
C.M. Bishop, Neural Networks for Pattern Recognition, Oxford: Clarendon Press, 1995, 482 p.
A. Cichocki, R. Unbehauen, Neural Networks for Optimization and Signal Processing, Stuttgart: Teubner, 1993, 526 p.
J. Han, M. Kamber, Data Mining: Concepts and Techniques, Amsterdam: Morgan Kaufman Publ., 2006, 743 p.
P. Mulesa, I. Perova, “Fuzzy spacial extrapolation method using Manhattan metrics for tasks of medical data mining,” Proceedings of the International Conference on Computer Science and Information Technologies CSIT’2015, Lviv, Ukraine, 2015, pp. 104-106. DOI: 10.1109/STC-CSIT.2015.7325443.
E. Oja, “A simplified neuron model as a principal component analyzer,” J. of Math. Biology, vol. 15, pp. 267-273, 1982.
E. Oja, “Principal component, minor components, and liner neural networks,” Neural Networks, vol. 5, pp. 927-935, 1992.
Iris Fisher dataset. [Online]. Available from: http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data. Accessed: 1st of May, 2008.
Dermatology dataset. [Online]. Available from: http://archive.ics.uci.edu/ml/machine-learning-databases/dermatology/dermatology.data. Accessed: 1st of May, 2008.
Breast Cancer in Wisconsin dataset. [Online]. Available from: http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data
Pima Indians Diabetes dataset. [Online]. Available from: http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data. Accessed: 1st of May, 2008.
Parkinson dataset. [Online]. Available from: http://archive.ics.uci.edu/ml/machine-learning-databases/parkinsons/parkinsons.data. Accessed: 1st of May, 2008.
I. Perova, Ye. Bodyanskiy, “Fast medical diagnostics using autoassociative neuro-fuzzy memory,” International Journal of Computing, vol. 16, issue 1, pp. 34-40, 2017.
I. Perova, I. Pliss, “Deep hybrid system of computational intelligence with architecture adaptation for medical fuzzy diagnostics,” I.J. Intelligent System and Applications, vol. 7, pp. 12-21, 2017. DOI: 10.5815/ijisa.2017. 07.02.
I. Pliss, I. Perova, “Diagnostic neuro-fuzzy system and its learning in medical data mining tasks in conditions of uncertainty about numbers of attributes and diagnoses,” Automatic Control and Computer Sciences, vol. 51, issue 6, pp. 391-398, 2017. DOI: 10.3103/ S0146411617060062.
Ye. Bodyanskiy, I. Perova, O. Vynokurova, I. Izonin, “Adaptive wavelet diagnostic neuro-fuzzy system for biomedical tasks,” Proceedings of the 14th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), Lviv-Slavske, Ukraine, February 20–24, 2018, pp. 299-303.
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