Survey of Fuzzy Min–Max Neural Network for Pattern Classification Variants and Applications

Over the last few decades, pattern classification has become one of the most important fields of artificial intelligence because it constitutes an essential component in many different real-world applications. Artificial neural networks and fuzzy logic are two most widely used models in pattern classification. To build an efficient and powerful model, researchers have introduced hybrid models that combine both fuzzy logic and artificial neural networks. Among the hybrid models, the fuzzy min–max (FMM) neural network has been proven to be a premier model for undertaking pattern classification problems. While FMM is useful in terms of its capability of online learning, it suffers from several limitations in the learning procedure. Therefore, over the past years, researchers have proposed numerous improvements to overcome the limitations of the original FMM model. This paper carries out a comprehensive survey of the developments conducted on the FMM model for pattern classification. In order to assist recent researchers in selecting the most suitable FMM variant and to provide proper guidance for future developments, this study divides the variants of FMM into two main board categories, namely FMM variants with and without contraction. This division facilitates understanding of the developments conducted by researchers on the original FMM neural network, as well as provides the scope to identify the limitations that still exist in the FMM models. This paper also summarizes the use of FMM and its variants in solving different benchmark and real-world problems. Finally, the possible future trends are highlighted.

[1]  Stephen Grossberg,et al.  Art 2: Self-Organization Of Stable Category Recognition Codes For Analog Input Patterns , 1988, Other Conferences.

[2]  Hyeran Byun,et al.  Applications of Support Vector Machines for Pattern Recognition: A Survey , 2002, SVM.

[3]  Chee Peng Lim,et al.  A modified fuzzy min-max neural network with rule extraction and its application to fault detection and classification , 2008, Appl. Soft Comput..

[4]  Karim Salahshoor,et al.  Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers , 2010 .

[5]  Guo Bing,et al.  Pattern Recognition and Classification for Tactile Sensor Based on Fuzzy Decision Tree , 2008, ACFIE.

[6]  Abhijeet V. Nandedkar.  An Interactive Shadow Detection and Removal Tool using Granular Reflex Fuzzy Min-Max Neural Network , .

[7]  Dazhong Ma,et al.  Data-Core-Based Fuzzy Min–Max Neural Network for Pattern Classification , 2011, IEEE Transactions on Neural Networks.

[8]  Roberto Tagliaferri,et al.  Fuzzy neural networks for classification and detection of anomalies , 1998, IEEE Trans. Neural Networks.

[9]  G. N. Shinde,et al.  Iris Recognition Using Fuzzy Min-Max Neural Network , 2011 .

[10]  Honghai Wang,et al.  Pattern Classification with Random Decision Forest , 2012, 2012 International Conference on Industrial Control and Electronics Engineering.

[11]  D. D. Doye,et al.  Speech Recognition using Modular General Fuzzy Min-Max Neural Network , 2002 .

[12]  J. Havel,et al.  Artificial neural networks in medical diagnosis , 2013 .

[13]  Chee Peng Lim,et al.  Authenticating the Identity of Computer Users with Typing Biometrics and the Fuzzy Min-Max Neural Network( BIOMETRICS AND ITS APPLICATIONS) , 2009, SOCO 2009.

[14]  Chee Peng Lim,et al.  Fault Detection and Diagnosis of Induction Motors Using Motor Current Signature Analysis and a Hybrid FMM–CART Model , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[15]  Chee Peng Lim,et al.  Patient admission prediction using a pruned fuzzy min–max neural network with rule extraction , 2014, Neural Computing and Applications.

[16]  Prabir Kumar Biswas,et al.  A General Reflex Fuzzy Min-Max Neural Network , 2007, Eng. Lett..

[17]  Hyun Seung Yang,et al.  A Weighted FMM Neural Network and Its Application to Face Detection , 2006, ICONIP.

[18]  Chee Peng Lim,et al.  Improving the Fuzzy Min-Max neural network with a K-nearest hyperbox expansion rule for pattern classification , 2017, Appl. Soft Comput..

[19]  Anice Jahanjoo,et al.  Accurate fall detection using 3-axis accelerometer sensor and MLF algorithm , 2017, 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA).

[20]  Andrzej Bargiela,et al.  An inclusion/exclusion fuzzy hyperbox classifier , 2004, Int. J. Knowl. Based Intell. Eng. Syst..

[21]  Chettiar Bharathi,et al.  Optimal Fuzzy Min-Max Neural Network (FMMNN) for Medical Data Classification Using Modified Group Search Optimizer Algorithm , 2016 .

[22]  Zezhi Chen,et al.  Road vehicle classification using Support Vector Machines , 2009, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[23]  Tara N. Sainath,et al.  FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .

[24]  Patrick K. Simpson,et al.  Fuzzy min-max neural networks. I. Classification , 1992, IEEE Trans. Neural Networks.

[25]  Yong Duan,et al.  State Space Partition for Reinforcement Learning Based on Fuzzy Min-Max Neural Network , 2007, ISNN.

[26]  Abhijeet V. Nandedkar,et al.  Fuzzy min-max neural network based translation, rotation and scale invariant character recognition using RTSI features , 2004, The Fourth International Conference onComputer and Information Technology, 2004. CIT '04..

[27]  Prabir Kumar Biswas,et al.  Object Recognition Using Reflex Fuzzy Min-Max Neural Network with Floating Neurons , 2006, ICVGIP.

[28]  Zhizeng Luo,et al.  Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors , 2017, Sensors.

[29]  Chee Peng Lim,et al.  Application of the Fuzzy Min-Max Neural Networks to Medical Diagnosis , 2008, KES.

[30]  Ming Yang,et al.  3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  H. Singh,et al.  Detection and classification of electrical supply voltage quality to electrical motors using the Fuzzy-Min-Max neural network , 2011, 2011 IEEE International Electric Machines & Drives Conference (IEMDC).

[32]  Kanchan Deshmukh.  Adaptive Color Image Segmentation Using Fuzzy Min-Max Clustering , 2006, Eng. Lett..

[33]  Rakesh K. Jambhulkar.  A Review on Pattern Classification Using Multilevel and Other Fuzzy Min Max Neural Network Classifier , 2014 .

[34]  L. M. Khanli,et al.  Intrusion Detection in the Cloud Environment Using Multi-Level Fuzzy Neural Networks , 2015 .

[35]  Patrick K. Simpson,et al.  Fuzzy min-max neural networks - Part 2: Clustering , 1993, IEEE Trans. Fuzzy Syst..

[36]  Yang Song,et al.  IKNN: Informative K-Nearest Neighbor Pattern Classification , 2007, PKDD.

[37]  Vijay Kumar Jha,et al.  A Novel Fuzzy Min-Max Neural Network and Genetic Algorithm-Based Intrusion Detection System , 2016 .

[38]  K. P. Rane,et al.  Online Signature Classification Using Modified Fuzzy Min-Max Neural Network with Compensatory Neuron Topology , 2010, IC3.

[39]  Vijay Kumar Jha,et al.  Fuzzy min–max neural network and particle swarm optimization based intrusion detection system , 2017 .

[40]  Aloysius George,et al.  On hybridizing fuzzy min max neural network and firefly algorithm for automated heart disease diagnosis , 2013, 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

[41]  Andrzej Bargiela,et al.  Adaptation of exclusion / inclusion hyperboxes for classification of complex data , 2004 .

[42]  Dipti Pawar.  Fuzzy min-max neural network with compensatory neuron architecture for invariant object recognition , 2015, 2015 International Conference on Computer, Communication and Control (IC4).

[43]  Anthony V. Robins,et al.  Catastrophic forgetting in neural networks: the role of rehearsal mechanisms , 1993, Proceedings 1993 The First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.

[44]  Haiyan Hu,et al.  Mining patterns in disease classification forests , 2010, J. Biomed. Informatics.

[45]  Chee Peng Lim,et al.  A Modified Fuzzy Min-Max Neural Network and Its Application to Fault Classification , 2007 .

[46]  Muharram Mansoorizadeh,et al.  Multi-Level Fuzzy Min-Max Neural Network Classifier , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[47]  Andrzej Bargiela,et al.  General fuzzy min-max neural network for clustering and classification , 2000, IEEE Trans. Neural Networks Learn. Syst..

[48]  G. R. Dattatreya,et al.  Bayesian and Decision Tree Approaches for Pattern Recognition Including Feature Measurement Costs , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[49]  M. Pal,et al.  Random forests for land cover classification , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[50]  Shiming Xiang,et al.  Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks , 2014, IEEE Geoscience and Remote Sensing Letters.

[51]  Mohammed Falah Mohammed,et al.  SAIRF: A similarity approach for attack intention recognition using fuzzy min-max neural network , 2017, J. Comput. Sci..

[52]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[53]  Paras A. Tolia.  Compensatory Fuzzy Min-Max Neural Network for Object Recognition , 2013 .

[54]  Houkuan Huang,et al.  Feature selection for text classification with Naïve Bayes , 2009, Expert Syst. Appl..

[55]  E. E. Eldukhri,et al.  Image segmentation using fuzzy min-max neural networks for wood defect detection , 2005 .

[56]  Ho Joon Kim,et al.  A Weighted Fuzzy Min-Max Neural Network for Pattern Classification and Feature Extraction , 2004, ICCSA.

[57]  Chee Peng Lim,et al.  Fault Detection and Diagnosis Using the Fuzzy Min-Max Neural Network with Rule Extraction , 2004, KES.

[58]  Manjeevan Seera,et al.  Detection and diagnosis of broken rotor bars and eccentricity faults in induction motors using the Fuzzy Min-Max neural network , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[59]  R. S. Holambe,et al.  Use of fuzzy min-max neural network for speaker identification , 2011, 2011 International Conference on Recent Trends in Information Technology (ICRTIT).

[60]  Ho Joon Kim,et al.  Human Action Recognition Using a Modified Convolutional Neural Network , 2007, ISNN.

[61]  Seong-Hoon Kim,et al.  X-ray Image Classification Using Random Forests with Local Wavelet-Based CS-Local Binary Patterns , 2011, Journal of Digital Imaging.

[62]  Hyun-Chul Kim,et al.  Pattern classification using support vector machine ensemble , 2002, Object recognition supported by user interaction for service robots.

[63]  S. Sri Abirami,et al.  Glaucoma Images Classification Using Fuzzy Min-Max Neural Network Based On Data-Core , 2022 .

[64]  Sharhabeel H. Alnabelsi,et al.  CERVICAL CANCER DIAGNOSTIC SYSTEM USING ADAPTIVE FUZZY MOVING K-MEANS ALGORITHM AND FUZZY MIN-MAX NEURAL NETWORK , 2013 .

[65]  Paolo Frasconi,et al.  Hidden Tree Markov Models for Document Image Classification , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[66]  Hamid Reza Boveiri.  Persian Printed Numeral Characters Recognition Using Geometrical Central Moments and Fuzzy Min-Max Neural Network , 2010 .

[67]  Xiukun Wei,et al.  Research on fault isolation of rail vehicle suspension system , 2015, The 27th Chinese Control and Decision Conference (2015 CCDC).

[68]  Yuanzhi Cheng,et al.  Computer-Aided Detection of Lung Nodules with Fuzzy Min-Max Neural Network for False Positive Reduction , 2014, 2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics.

[69]  Chee Peng Lim,et al.  Offline and online fault detection and diagnosis of induction motors using a hybrid soft computing model , 2013, Appl. Soft Comput..

[70]  Ananth N. Iyer,et al.  Sequential k-nearest neighbor pattern recognition for usable speech classification , 2004, 2004 12th European Signal Processing Conference.

[71]  Dazhong Ma,et al.  An Adaptive Fuzzy Min-Max Neural Network Classifier Based on Principle Component Analysis and Adaptive Genetic Algorithm , 2012 .

[72]  Nicu Sebe,et al.  Emotion recognition using a Cauchy Naive Bayes classifier , 2002, Object recognition supported by user interaction for service robots.

[73]  John J. Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities , 1999 .

[74]  Prabir Kumar Biswas,et al.  A Granular Reflex Fuzzy Min–Max Neural Network for Classification , 2009, IEEE Transactions on Neural Networks.

[75]  D. B. Kshirsagar,et al.  A generalized Neuro-Fuzzy Based Image Retrieval system with modified colour coherence vector and Texture element patterns , 2016, 2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT).

[76]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[77]  Suprit Kulkarni,et al.  Review on Classification and Clustering using Fuzzy Neural Networks , 2016 .

[78]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[79]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[80]  Ju H. Park,et al.  An Asynchronous Operation Approach to Event-Triggered Control for Fuzzy Markovian Jump Systems With General Switching Policies , 2018, IEEE Transactions on Fuzzy Systems.

[81]  Luo Fei,et al.  An Improved FMM Neural Network for Classification of Gene Expression Data , 2007, ICFIE.

[82]  Muhammad Khusairi Osman,et al.  Weather Forecasting Using Photovoltaic System and Neural Network , 2010, 2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks.

[83]  Chee Peng Lim,et al.  Application of the fuzzy min–max neural network to fault detection and diagnosis of induction motors , 2012, Neural Computing and Applications.

[84]  Seba Susan,et al.  Fuzzy Min-Max Neural Networks for Business Intelligence , 2013, 2013 International Symposium on Computational and Business Intelligence.

[85]  Bhupendra M. Chaudhari,et al.  Signature recognition using fuzzy min-max neural network , 2009, 2009 International Conference on Control, Automation, Communication and Energy Conservation.

[86]  Juho Lee,et al.  Face detection using an adaptive skin-color filter and FMM neural networks , 2006 .

[87]  Shraddha Deshmukh,et al.  Diagnosis of Lung Cancer using Pruned Fuzzy Min-Max Neural Network , 2016, 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT).

[88]  Sebastián Lozano,et al.  Cell formation using a Fuzzy Min-Max neural network , 2002 .

[89]  Chee Peng Lim,et al.  Online Motor Fault Detection and Diagnosis Using a Hybrid FMM-CART Model , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[90]  Prabir Kumar Biswas,et al.  A Fuzzy Min-Max Neural Network Classifier With Compensatory Neuron Architecture , 2007, IEEE Transactions on Neural Networks.

[91]  Chee Peng Lim,et al.  A Modified Fuzzy Min–Max Neural Network With a Genetic-Algorithm-Based Rule Extractor for Pattern Classification , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[92]  Din-Chang Tseng,et al.  Invariant handwritten Chinese character recognition using fuzzy min-max neural networks , 1997, Pattern Recognit. Lett..

[93]  Hamid Reza Karimi,et al.  A Flexible Terminal Approach to Sampled-Data Exponentially Synchronization of Markovian Neural Networks With Time-Varying Delayed Signals , 2018, IEEE Transactions on Cybernetics.

[94]  Suja S. Panicker,et al.  Use of Fuzzy C-Mean and Fuzzy Min-Max Neural Network in Lung Cancer Detection , 2013 .

[95]  Chee Peng Lim,et al.  An Enhanced Fuzzy Min–Max Neural Network for Pattern Classification , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[96]  Stephen Grossberg,et al.  Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system , 1991, Neural Networks.

[97]  N P Jawarkar.  Emotion Recognition using Prosody Features and a Fuzzy Min-Max Neural Classifier , 2007 .

[98]  L. Satish,et al.  Use of hidden Markov models for partial discharge pattern classification , 1993 .

[99]  Chee Peng Lim,et al.  A new hyperbox selection rule and a pruning strategy for the enhanced fuzzy min-max neural network , 2017, Neural Networks.

[100]  Michael McCloskey,et al.  Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem , 1989 .