Classification with Fuzzification Optimization Combining Fuzzy Information Systems and Type-2 Fuzzy Inference

In this research, we introduce a classification procedure based on rule induction and fuzzy reasoning. The classifier generalizes attribute information to handle uncertainty, which often occurs in real data. To induce fuzzy rules, we define the corresponding fuzzy information system. A transformation of the derived rules into interval type-2 fuzzy rules is provided as well. The fuzzification applied is optimized with respect to the footprint of uncertainty of the corresponding type-2 fuzzy sets. The classification process is related to a Mamdani type fuzzy inference. The method proposed was evaluated by the F-score measure on benchmark data.

[1]  Jerry M. Mendel,et al.  Type-2 fuzzy sets made simple , 2002, IEEE Trans. Fuzzy Syst..

[2]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[3]  Wenbin Chen,et al.  An immune-inspired semi-supervised algorithm for breast cancer diagnosis , 2016, Comput. Methods Programs Biomed..

[4]  D. Dubois,et al.  ROUGH FUZZY SETS AND FUZZY ROUGH SETS , 1990 .

[5]  Qinghua Hu,et al.  Fuzzy information systems and their homomorphisms , 2014, Fuzzy Sets Syst..

[6]  Paulo Vitor de Campos Souza,et al.  Uninorm based regularized fuzzy neural networks , 2018, 2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS).

[7]  Zdzislaw Pawlak,et al.  Information systems theoretical foundations , 1981, Inf. Syst..

[8]  Xizhao Wang,et al.  On the generalization of fuzzy rough sets , 2005, IEEE Transactions on Fuzzy Systems.

[9]  I-Cheng Yeh,et al.  Knowledge discovery on RFM model using Bernoulli sequence , 2009, Expert Syst. Appl..

[10]  Vanessa Souza Araujo,et al.  Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy , 2019, Big Data Cogn. Comput..

[11]  Dongrui Wu,et al.  On the Fundamental Differences Between Interval Type-2 and Type-1 Fuzzy Logic Controllers , 2012, IEEE Transactions on Fuzzy Systems.

[12]  Zdzisław Pawlak,et al.  Rough sets. Basic notions , 1981 .

[13]  Aan Kardiana,et al.  Breast Cancer Diagnosis using Artificial Neural Networks with Extreme Learning Techniques , 2019 .

[14]  Damodar Reddy Edla,et al.  RST-BatMiner: A fuzzy rule miner integrating rough set feature selection and Bat optimization for detection of diabetes disease , 2017, Appl. Soft Comput..

[15]  Dongrui Wu,et al.  Approaches for Reducing the Computational Cost of Interval Type-2 Fuzzy Logic Systems: Overview and Comparisons , 2013, IEEE Transactions on Fuzzy Systems.

[16]  Mehrbakhsh Nilashi,et al.  A knowledge-based system for breast cancer classification using fuzzy logic method , 2017, Telematics Informatics.

[17]  Dongrui Wu,et al.  GENETIC LEARNING AND PERFORMANCE EVALUATION OF TYPE-2 FUZZY LOGIC CONTROLLERS , 2006 .

[18]  Yuwen Li,et al.  Attribute reduction for multi-label learning with fuzzy rough set , 2018, Knowl. Based Syst..

[19]  Qiang Shen,et al.  Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches , 2004, IEEE Transactions on Knowledge and Data Engineering.

[20]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[21]  Ming-Chang Lee,et al.  Rule Extraction Based on Rough Fuzzy Sets in Fuzzy Information Systems , 2011, Trans. Comput. Collect. Intell..

[22]  Saeid Nahavandi,et al.  An expert system for selecting wart treatment method , 2017, Comput. Biol. Medicine.

[23]  Jianhua Dai,et al.  Feature selection via normative fuzzy information weight with application into tumor classification , 2020, Appl. Soft Comput..

[24]  Jerry M. Mendel,et al.  Type-2 fuzzy logic systems , 1999, IEEE Trans. Fuzzy Syst..

[25]  Paulo Vitor de Campos Souza,et al.  Pulsar Detection for Wavelets SODA and Regularized Fuzzy Neural Networks Based on Andneuron and Robust Activation Function , 2019, Int. J. Artif. Intell. Tools.

[26]  Anindya Halder,et al.  Active learning using rough fuzzy classifier for cancer prediction from microarray gene expression data , 2019, J. Biomed. Informatics.

[27]  Ying Daisy Zhuo,et al.  Robust Classification , 2019, INFORMS J. Optim..

[28]  Dongrui Wu,et al.  Comparison and practical implementation of type-reduction algorithms for type-2 fuzzy sets and systems , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

[29]  Dongrui Wu.  An overview of alternative type-reduction approaches for reducing the computational cost of interval type-2 fuzzy logic controllers , 2012, 2012 IEEE International Conference on Fuzzy Systems.

[30]  Wei-Zhi Wu,et al.  Generalized fuzzy rough sets , 2003, Inf. Sci..

[31]  F. Kayaalp,et al.  A Hybrid Classification Example in the Diagnosis of Skin Disease with Cryotherapy and Immunotherapy Treatment , 2018, 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT).

[32]  Guangji Yu,et al.  Characterizations and uncertainty measurement of a fuzzy information system and related results , 2020, Soft Computing.

[33]  Chris Cornelis,et al.  Fuzzy Rough Sets: The Forgotten Step , 2007, IEEE Transactions on Fuzzy Systems.

[34]  Andrzej Skowron,et al.  A rough set approach to real-time state identification , 1993, Bull. EATCS.

[35]  Anil Kumar Dudyala,et al.  Bank note authentication using decision tree rules and machine learning techniques , 2015, 2015 International Conference on Advances in Computer Engineering and Applications.

[36]  Chris Cornelis,et al.  Applications of Fuzzy Rough Set Theory in Machine Learning: a Survey , 2015, Fundam. Informaticae.

[37]  Z. Pan,et al.  A hybrid ensemble method for pulsar candidate classification , 2019, Astrophysics and Space Science.

[38]  J. Mendel.  Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions , 2001 .

[39]  Jerry M. Mendel,et al.  Enhanced Karnik--Mendel Algorithms , 2009, IEEE Transactions on Fuzzy Systems.

[40]  Partha Garai,et al.  Fuzzy–Rough Simultaneous Attribute Selection and Feature Extraction Algorithm , 2013, IEEE Transactions on Cybernetics.

[41]  Dongrui Wu,et al.  Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers , 2006, Eng. Appl. Artif. Intell..

[42]  Paulo Vitor de Campos Souza,et al.  Pruning fuzzy neural networks based on unineuron for problems of classification of patterns , 2018, J. Intell. Fuzzy Syst..

[43]  Kojin Oshiba,et al.  Robust Classification of Financial Risk , 2018, ArXiv.

[44]  Witold Pedrycz,et al.  Type-2 Fuzzy Logic: Theory and Applications , 2007, 2007 IEEE International Conference on Granular Computing (GRC 2007).

[45]  Aytug Onan,et al.  A fuzzy-rough nearest neighbor classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer , 2015, Expert Syst. Appl..

[46]  Hani Hagras,et al.  A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots , 2004, IEEE Transactions on Fuzzy Systems.

[47]  Mehmet Fatih Akay,et al.  Support vector machines combined with feature selection for breast cancer diagnosis , 2009, Expert Syst. Appl..

[48]  Jianming Zhan,et al.  A novel decision-making approach based on three-way decisions in fuzzy information systems , 2020, Inf. Sci..

[49]  Didier Dubois,et al.  The role of fuzzy sets in decision sciences: Old techniques and new directions , 2011, Fuzzy Sets Syst..

[50]  Max A. Little,et al.  Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection , 2007, Biomedical engineering online.

[51]  Alicja Mieszkowicz-Rolka,et al.  Fuzziness in Information Systems , 2003, RSKD.

[52]  Joshua D. Knowles,et al.  Fifty years of pulsar candidate selection: from simple filters to a new principled real-time classification approach , 2016, Monthly Notices of the Royal Astronomical Society.

[53]  H. Hagras,et al.  Type-2 FLCs: A New Generation of Fuzzy Controllers , 2007, IEEE Computational Intelligence Magazine.