Abstract
Massive and complex data are generated every day in many fields. Complex data refer to data sets that are so large that conventional database management and data analysis tools are insufficient to deal with them. Managing and analysis of medical big data involve many different issues regarding their structure, storage and analysis. In this paper, linguistic hedges neuro-fuzzy classifier with selected features (LHNFCSF) is presented for dimensionality reduction, feature selection and classification. Four real-world data sets are provided to demonstrate the performance of the proposed neuro-fuzzy classifier. The new classifier is compared with the other classifiers for different classification problems. The results indicated that applying LHNFCSF not only reduces the dimensions of the problem, but also improves classification performance by discarding redundant, noise-corrupted, or unimportant features. The results strongly suggest that the proposed method not only help reducing the dimensionality of large data sets but also can speed up the computation time of a learning algorithm and simplify the classification tasks.
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Assad AH, Azar AT, Hassanien AE (2014) A comparative study on feature selection for retinal vessel segmentation using ant colony system. Recent Adv Intell Inf Adv Intell Syst Comput 235(2014):1–11. doi:10.1007/978-3-319-01778-5_1
Azar AT, El-Said SA (2013) Superior neuro-fuzzy classification systems. Neural Comput Appl 23(1):55–72. doi:10.1007/s00521-012-1231-8 (Springer)
Azar AT (2013a) Multi-adaptive neural-fuzzy system as a novel predictor of in-vivo blood side dialyzer urea clearance. Int J Comput Appl Technol 46(2):77–90
Azar AT (2013b) Adaptive network based on fuzzy inference system for equilibrated urea concentration prediction. Comput Methods Progr Biomed 111(3):578–591 (Elsevier)
Azar AT (2013c) A novel ANFIS application for prediction of post-dialysis blood urea concentration. Int J Intell Syst Technol Appl (IJISTA) 12(2):87–110
Azar AT (2013d) Neuro-fuzzy applications in dialysis systems. In: Azar AT (ed) Biofeedback systems and soft computing techniques of dialysis, vol 405. Springer-Verlag GmbH, Berlin, Heidelberg, pp 1223–1274. doi:10.1007/978-3-642-27558-6_10
Azar AT, El-Said SA, Balas VE, Olariu T (2013a) Linguistic hedges fuzzy feature selection for erythemato-squamous diseases. In: Balas VE, Fodor J, Várkonyi-Kóczy AR, Dombi J, Jain LC (eds) Soft computing applications, advances in intelligent systems and computing (AISC), vol 195. Springer, Berlin, Heidelberg, pp 487–500. doi:10.1007/978-3-642-33941-7_43
Azar AT, Banu PKN and Inbarani HH (2013b) PSORR—an unsupervised feature selection technique for fetal heart rate. In: 5th international conference on modelling, identification and control (ICMIC 2013), 31 August. Egypt, 1–2 Sept 2013
Azar AT, Hassanien AE, Kim TH (2012) Expert system based on neural-fuzzy rules for thyroid diseases diagnosis. In: International conference on bio-science and bio-technology (BSBT 2012), 16–19 Dec 2012, Korea, vol 353 of the communications in computer and information science series. Springer, pp 94–105. ISBN:978-3-642-35520-2. doi:10.1007/978-3-642-35521-9_13
Azar AT (2012) Overview of type-2 fuzzy logic systems. Int J Fuzzy Syst Appl (IJFSA) 2(4):1–28
Azar AT (2010a) Fuzzy systems. IN-TECH, Vienna
Azar AT (ed) (2010b) Adaptive neuro-fuzzy systems. Fuzzy systems. IN-TECH, Austria, pp 85–110
Azar AT (2011) Neuro-fuzzy system for cardiac signals classification. Int J Model Identif Control (IJMIC) 13(1/2):108–116
Bache K, Lichman M, (2013) UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine. http://archive.ics.uci.edu/ml. Accessed 04 June 2014
Banks W (1994) Mixing crisp and fuzzy logic in applications. WESCON’94 Idea/ microelectronics conference record, Anaheim, pp 94–97
Benecchi L (2009) Neuro-fuzzy system for prostate cancer diagnosis. Urology 68(2):357–361
Bouchon-Meunier B (1992) Linguistic hedges and fuzzy logic. In: Proceedings of the first IEEE international conference on fuzzy systems, San Diego, pp 247–254
Casillas J, Cordon O, Del Jesus MJ, Herrera F (2005) Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction. IEEE Trans Fuzzy Syst 13(1):13–29
Cetişli B (2010a) Development of an adaptive neuro-fuzzy classifier using linguistic hedges: part 1. Expert Syst Appl 37(8):6093– 6101
Cetişli B (2010b) The effect of linguistic hedges on feature selection: part 2. Expert Syst Appl 37(8):6102–6108
Chatterjee A, Siarry P (2007) A PSO-aided neuro-fuzzy classifier employing linguistic hedge concepts. Expert Syst Appl 33(4):1097–1109
Cheng HD, Cai X, Chen X, Hu L, Lou X (2003) Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recognit 36(12):2967–2991
Dash M, Liu H (2003) Consistency-based search in feature selection. Artif Intell 151(1–2):155–176
De Cock M, Kerre EE (2004) Fuzzy modifiers based on fuzzy relations. Inf Sci 160(1–4):173–199
Gheyas IA, Smith LS (2010) Feature subset selection in large dimensionality domains. Pattern Recognit 43(1):5–13
Hastie T, Tibshirani R, Friedman JH (2001) The elements of statistical learning, 2nd edn. Springer, Berlin
Ho NC, Wechler W (1992) Extended hedge algebras and their application to fuzzy logic. Fuzzy Sets Syst 52(3):259–281
Huynh VN, Ho TB, Nakamori Y (2002) A parametric representation of linguistic hedges in Zadeh’s fuzzy logic. Int J Approx Reason 30(3):203–223
Inbarani HH, Jothi G, Azar AT (2013) Hybrid tolerance-PSO based supervised feature selection for digital mammogram images. Int J Fuzzy Syst Appl (IJFSA) 3(4):15–30
Inbarani HH, Azar AT, Jothi G (2014a) Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis. Comput Methods Progr Biomed 113(1):175–185
Inbarani HH, Banu PKN, Azar AT (2014b) Feature selection using swarm-based relative reduct technique for fetal heart rate. Neural Computing and Applications. doi:10.1007/s00521-014-1552-x (Springer)
Jang JSR (1992) Neuro-fuzzy modeling: architectures, analyses, and applications. Ph.D. dissertation, EECS Department, University of California at Berkeley
Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685
Jang JSR, Sun CT (1995) Neuro-fuzzy modeling and control. Proc IEEE 83(3):378–406
Jang JSR, Sun CT, Mizutani E (1997) Neuro-Fuzzy and soft computing. Prentice-Hall, Englewood Cliffs
Liu BD, Chen CY, Tsao JY (2001) Design of adaptive fuzzy logic controller based on linguistic-hedge concepts and genetic algorithms. IEEE Trans Syst Man Cybern Part B 31(1):32–53
Mangasarian OL, Street WN, Wolberg WH (1995) Breast cancer diagnosis and prognosis via linear programming. Oper Res 43(4):570–577
Mitra S, Hayashi Y (2000) Neuro-fuzzy rule generation: survey in soft computing framework. IEEE Trans Neural Netw 11(3):748–757
Mitra P, Murthy CA, Pal SK (2002) Unsupervised feature selection using feature similarity. IEEE Trans Pattern Anal Mach Intell 24(3):301–312
Moller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6(4):525–533
Nieto J, Torres A (2003) Midpoint for fuzzy sets and their application in medicine. Artif Intell Med 27(1):321–355
Novak V (1996) A horizon shifting model of linguistic hedges for approximate reasoning. In: Proceedings of the fifth IEEE international conference on fuzzy systems, pp 423–427
Oh II-S, Lee JS, Moon BR (2004) Hybrid genetic algorithms for feature selection. IEEE Trans Pattern Anal Mach Intell 26(11):1424–1437
Peng Y, Wu Z, Jiang J (2010) A novel feature selection approach for biomedical data classification. J Biomed Inform 43(1):15–23
Robnik-Sikonja M, Kononenko I (2003) Theoretical and empirical analysis of Relief and ReliefF. Mach Learn 53(1–2):23–69
Ruiz R, Riquelme JC, Aguilar-Ruiz JS, Garcia-Torres M (2012) Fast feature selection aimed at high-dimensional data via hybrid-sequential-ranked searches. Expert Syst Appl 39(12):11094–11102
Russo M, Jain L (2001) Fuzzy learning and application. Prentice-Hall, Englewood Cliffs
Saeys Y, Inza I, Larranaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23(19):2507–2517
Sun CT, Jang JSR (1993) A neuro-fuzzy classifier and its applications. Proc IEEE Int Conf Fuzzy Syst San Francisco 1:94–98
Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15(1):116–132
Verma K, Zakos J (2001) A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques. IEEE Trans Inf Technol Biomed 5(1):46–54
Wolberg WH, Street WN, Mangasarian OL (1995) Image analysis and machine learning applied to breast cancer diagnosis and prognosis. Anal Quant Cytol Histol 17(2):77–87
Zadeh LA (1965) Fuzzy sets. Inform Contr 8(3):338–353
Zadeh LA (1968) Fuzzy algorithm. Inf Control 12(2):94–102
Zadeh LA (1972) A fuzzy-set—theoretic interpretation of linguistic hedges. J Cybern 2(3):4–34
Zadeh LA (1973) Outline of a new approach to the analysis of complex system and decision processes. IEEE Trans Syst Man Cybern 3(1):28–44
Zadeh LA (1983a) Commonsense knowledge representation based on fuzzy logic. IEEE Comput 16(10):61–65
Zadeh LA (1983b) Fuzzy logic. IEEE Comput 1(4):83–93
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Azar, A.T., Hassanien, A.E. Dimensionality reduction of medical big data using neural-fuzzy classifier. Soft Comput 19, 1115–1127 (2015). https://doi.org/10.1007/s00500-014-1327-4
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DOI: https://doi.org/10.1007/s00500-014-1327-4