caa a22 4500 605469903 CHVBK 20210128100325.0 cr unu---uuuuu 210128e20150401xx s 000 0 eng 10.1007/s00500-014-1327-4 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1327-4 Dimensionality reduction of medical big data using neural-fuzzy classifier [Elektronische Daten] [Ahmad Azar, Aboul Hassanien] 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. Springer-Verlag Berlin Heidelberg, 2014 Takagi-Sugeno-Kang (TSK) fuzzy inference system nationallicence Adaptive neuro-fuzzy inference system (ANFIS) nationallicence Linguistic hedge (LH) nationallicence Feature selection (FS) nationallicence Azar Ahmad Faculty of Computers and Information, Benha University, Banha, Egypt aut Hassanien Aboul Faculty of Computers and Information, Cairo University, Giza, Egypt aut Soft Computing Springer Berlin Heidelberg 19/4(2015-04-01), 1115-1127 1432-7643 19:4<1115 2015 19 500 https://doi.org/10.1007/s00500-014-1327-4 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1327-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Azar Ahmad Faculty of Computers and Information, Benha University, Banha, Egypt aut NATIONALLICENCE 700 1- Hassanien Aboul Faculty of Computers and Information, Cairo University, Giza, Egypt aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/4(2015-04-01), 1115-1127 1432-7643 19:4<1115 2015 19 500