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A meta-cognitive interval type-2 fuzzy inference system and its projection based learning algorithm

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Abstract

A meta-cognitive interval type-2 neuro-fuzzy inference system (McIT2FIS) based classifier and its projection based learning algorithm is presented in this paper. McIT2FIS consists of two components, namely, a cognitive component and a meta-cognitive component. The cognitive component is an interval type-2 neuro-fuzzy inference system (IT2FIS) represented as a six layered adaptive network realizing Takagi-Sugeno-Kang type inference mechanism. A self-regulatory learning mechanism forms the meta-cognitive component. IT2FIS begins with zero rules, and rules are added and updated depending on the prediction error and relative knowledge contained the current sample. As each sample is presented to the network, the meta-cognitive component monitors the hinge-loss error and class-specific spherical potential of the current sample to decide what-to-learn, when-to-learn and how-to-learn them, efficiently. When a new rule is added or when an existing rule is updated, a projection based learning algorithm computes the optimal output weights with least computational effort by finding analytical minima of the nonlinear energy function. It uses class specific criterion and sample overlap criterion to estimate the network parameters corresponding to the minimum energy point of the error function. Moreover, consistently under - performing rules are pruned from the network leading to a compact network. The performance of McIT2FIS is first evaluated on a set of benchmark classification problems from UCI machine learning repository. A tenfold cross validation based performance comparison with other state-of-the-art approaches indicates its improved performance. Next, its performance is evaluated on detection of attention deficiency hyperactivity disorder (ADHD) in children. The aim of this study is to classify a child as having typically developing controls or as an ADHD patient. Voxel based features extracted from amygdala region of the brain is employed in this study. The network is trained and tested on samples obtained from ADHD-200 consortium dataset consisting of 941 subjects. The performance comparison with standard support vector machine shows that McIT2FIS has superior classification ability than SVM in diagnosing ADHD.

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References

  • Abiyev RH, Kaynak O, Alshanableh T, Mamedov F (2011) A type-2 neuro-fuzzy system based on clustering and gradient techniques applied to system identification and channel equalization. Appl Soft Comput 11(1):1396–1406

    Article  Google Scholar 

  • Angelov P (2011) Fuzzily connected multimodel systems evolving autonomously from data streams. Syst Man Cybern Part B: Cybern IEEE Trans 41(4):898–910

    Article  Google Scholar 

  • Angelov P, Filev P (2004a) An approach to online identification of Takagi-Sugeno fuzzy models. IEEE Trans Syst Man Cybern Part B: Cybern 34(1):484–498

    Article  Google Scholar 

  • Angelov P, Filev P (2004b) An approach to online identification of Takagi-Sugeno fuzzy models. IEEE Trans Syst Man Cybern Part B: Cybern 34(1):484–498

    Article  Google Scholar 

  • Angelov P, Filev P (2005) Simpl_eTS: a simplifed method for learning evolving Takagi-Sugeno fuzzy models. IEEE Int Conf Fuzzy Syst:1068–1073

  • Angelov P, Lughofer E, Zhou X (2008) Evolving fuzzy classifiers using different model architectures. Fuzzy Sets Syst 159(23):3160–3182

    Article  MATH  MathSciNet  Google Scholar 

  • Ashburner J (2007) A fast diffeomorphic image registration algorithm. NeuroImage 38(1):95–113

    Article  Google Scholar 

  • Babu G, Suresh S (2013a) Meta-cognitive RBF network and its projection based learning algorithm for classification problems. Appl Soft Comput 13(1):654–666

    Article  Google Scholar 

  • Babu G, Suresh S (2013b) Sequential projection based metacognitive learning in a radial basis function network for classification problems. IEEE Trans Neural Netw Learn Syst 24(2):194–206

    Article  Google Scholar 

  • Banaschewski T, Becker K, Scherag S, Franke B, Coghill D (2010) molecular genetics of attention-deficit/hyperactivity disorder: an overview. Eur Child Adolesc Psychiatry 19(3):237–257

    Article  Google Scholar 

  • Blake C, Merz C (1998) UCI repository of machine learning databases. http://archive.ics.uci.edu/ml/. Department of Information and Computer Sciences, University of California, Irvine

  • Bledsoe JC, Semrud-Clikeman M, Pliszka SR (2011) Neuroanatomical and neuropsychological correlates of the cerebellum in children with attention-defcit/hyperactivity disorder-combined type. J Am Acad Child Adolesc Psychiatry 50(6):593–601

    Article  Google Scholar 

  • Castillo O, Melin P (2012) A review on the design and optimization of interval type-2 fuzzy controllers. Appl Soft Comput 12(4):1267–1278

    Article  Google Scholar 

  • Castro JR, Castillo O, Melin P, Rodriguez-Diaz A (2009) A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks. Inf Sci 179(13):2175–2193

    Article  MATH  Google Scholar 

  • Chang C, Lin C (2003) LIBSVM: a library for support vector machines. http://www.csie.ntu.edu.tw/cjlin/libsvm/. National Taiwan University, Taiwan, Deptartment of Computer Science and Information Engineering

  • Cherkasova LHMV (2009) Neuroimaging in attention defcit hyperactivity disorder: beyond the frontostriatal circuitry. Can J Psychiatry 54(10):651–664

    Google Scholar 

  • Cortese S (2012) The neurobiology and genetics of attention-deficit/hyperactivity disorder (ADHD): what every clinician should know. Eur J Paediatr Neurol 16(5):422–433

    Article  Google Scholar 

  • Frodl T, Skokauskas N (2012) Meta-analysis of structural MRI studies inchildren and adults with attention defcit hyperactivity disorder indicates treatment efects. Acta Psychiatr Scand 125(2):114–126

    Article  Google Scholar 

  • Frodl T, Stauber J, Schaaff N, Koutsouleris N, Scheuerecker J, Ewers M, Omerovic M, Opgen-Rhein M, Hampel H, Reiser M, Moller HJ, Meisenzahl E (2010) Amygdala reduction in patients with ADHD compared with major depression and healthy volunteers. Acta Psychiatr Scand 121(2):111–118

    Article  Google Scholar 

  • Giedd JN, Rapoport JL (2010) Structural MRI of pediatric brain development: what have we learned and where are we going? Can J Psychiatry 67(5):728–734

    Google Scholar 

  • Huang G, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B: Cybern 42(2): 513–529

    Article  Google Scholar 

  • Ivanov I, Bansal R, Hao X, Zhu H, Kellendonk C, Miller L (2010) Morphological abnormalities of the thalamus in youths with attention defcit hyperactivity disorder. Am J Psychiatry 167(4):397–408

    Article  Google Scholar 

  • Jack CR, Petersen RC, Brien PCO, Tangalos EG (1992) Mr-based hippocampal volumetry in the diagnosis of alzheimer’s disease. Neurology 42(1):183–8

    Article  Google Scholar 

  • Jang J (1993) ANFIS: adaptive network based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Article  Google Scholar 

  • Juang CF (2008) A self-evolving interval type-2 fuzzy neural network with online structure and parameter learning. IEEE Trans Fuzzy Syst 16(6):1411–1424

    Article  Google Scholar 

  • Juang CF (2010) An interval type-2 fuzzy neural network with support vector regerssion for noisy regression problems. IEEE Trans Fuzzy Syst 18(4):686–699

    Article  Google Scholar 

  • Juang CF, Lin CT (2002) An on-line self-constructing neural fuzzy inference network and its applications. IEEE Trans Fuzzy Syst 10(2):144–154

    Article  Google Scholar 

  • Kayacan E, Oniz Y, Aras AC, Kaynak O, Abiyev R (2011) A servo system control with time varying and nonlinear load conditions using type-2 tsk fuzzy neural system. Appl Soft Comput 11(8):5735–5744

    Article  Google Scholar 

  • Leite D, Ballini R, Costa P, Gomide F (2012) Evolving fuzzy granular modeling from nonstationary fuzzy data streams. Evol Syst 3(2):65–79

    Article  Google Scholar 

  • Liang Q, Mendel J (2000) Interval type-2 fuzzy logic systems: Theory and design. IEEE Trans Fuzzy Syst 8(5):535–550

    Article  Google Scholar 

  • Lucas L, Centeno T, Delgado M (2007) General type-2 fuzzy inference systems: analysis, design and computational aspects. In: Fuzzy Systems Conference, pp 1–6

  • Lughofer E (2008) FLEXFIS: a robust incremental learning approach for evolving takagi sugeno fuzzy models. Fuzzy Syst IEEE Trans 16(6):1393–1410

    Article  Google Scholar 

  • Lughofer E (2012) A dynamic split-and-merge approach for evolving cluster models. Evol Syst 3(3):135–151

    Article  Google Scholar 

  • Lughofer E, Angelov P (2011) Handling drifts and shifts in on-line data streams with evolving fuzzy systems. Appl Soft Comput 11(2):2057–2068

    Article  Google Scholar 

  • Maciel L, Lemos A, Gomide F, Ballini R (2012) Evolving fuzzy systems fro pricing fixed income options. Evol Syst 3(1):5–18

    Article  Google Scholar 

  • Mahanand BS, Savitha R, Suresh S (2013) Computer aided diagnosis of ADHD using brain magnetic resonance images. LNCS 8272:386–395

    Google Scholar 

  • Mendel J (2007a) Advances in type-2 fuzzy sets and systems. Inf Sci 177(1):84–110

    Article  MATH  MathSciNet  Google Scholar 

  • Mendel J (2007b) Type-2 fuzzy sets and systems: an overview. IEEE Comput Intell Mag 2(1):20–29

    Article  MathSciNet  Google Scholar 

  • Milham PM, Damien F, Maarten M, Stewart HM (2012) The ADHD- 200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front Syst Neurosci 6:1–5

    Google Scholar 

  • Nelson TO, Narens L (1990) Metamemory: A theoretical framework and new findings. Psychol Learn Mot 26(C):125–173

    Article  Google Scholar 

  • Polanczyk G, Lima MSD, Horta BL, Biederman J, Rohde LA (2007) The worldwide prevalence of ADHD: a systematic review and metaregression analysis. Am J Psychiatry 164(6):942–948

    Article  Google Scholar 

  • Rong H, Sundararajan N, Huang G, Saratchandran P (2006) Sequential adaptive fuzzy inference system SAFIS for nonlinear system identification and prediction. Fuzzy Sets Syst 157(9):1260–1275

    Article  MATH  MathSciNet  Google Scholar 

  • Rong HJ, Huang GB, Sundararajan N, Saratchandran P (2009) Online sequential fuzzy extreme learning machine for function approximation and classification problems. IEEE Trans Syst Man Cybern Part B: Cybern 39(4):1067–1072

    Article  Google Scholar 

  • Rubio JJ (2009) SOFMLS: online self-organizing fuzzy modified least-squares network. Fuzzy Syst IEEE Trans 17(6):1296–1309

    Article  MathSciNet  Google Scholar 

  • Rubio JJ (2012) Modified optimal control with a back propagation network for robotic arms. Control Theory Appl IET 6(14):2216–2225

    Article  MathSciNet  Google Scholar 

  • Rubio JJ (2014) Evolving intelligent algorithms for the modelling of brain and eye signals. Appl Soft Comput 14(B):259–268

    Article  Google Scholar 

  • Rubio JJ, Humberto Perez-Cruz J (2014) Evolving intelligent system for the modelling of nonlinear systems with dead-zone input. Appl Soft Comput 14(B):289–304

    Article  Google Scholar 

  • Sateesh Babu G, Suresh S (2012) Meta-cognitive neural network for classification problems in a sequential learning framework. Neurocomputing 81(1):86–96

    Article  Google Scholar 

  • Sateesh Babu G, Suresh S (2013) Parkinson’s disease prediction using gene expression—a projection based learning meta-cognitive neural classifier approach. Expert Syst Appl 40(5):1519–1529

    Article  Google Scholar 

  • Savitha R, Suresh S, Sundararajan N (2012a) A meta-cognitive learning algorithm for a fully complex-valued relaxation network. Neural Netw 32(Special Issue):209–218

    Article  MATH  Google Scholar 

  • Savitha R, Suresh S, Sundararajan N (2012b) Metacognitive learning in a fully complex valued radial basis function neural network. Neural Comput 24(5):1297–1328

    Article  MathSciNet  Google Scholar 

  • Savitha R, Suresh S, Sundararajan N (2013) Projection based fast learning fully complex-valued relaxation neural network. IEEE Trans Neural Netw Learn Syst 24(4):529–541

    Article  Google Scholar 

  • Shaw P, Lerch J, Greenstein D, Sharp W, Clasen L, Evans A, Giedd J, Castellanos F, Rapoport J (2006) Longitudinal mapping of cortical thickness and clinical outcome in children and adolescents with attention-defcit/hyperactivity disorder. Arch Gen Psychiatry 63(5):540–549

    Article  Google Scholar 

  • Song Q, Kasabov N (2002) Dynamic evolving neuro-fuzzy inference system DENFIS: Online learning and application for time-series prediction. IEEE Trans Fuzzy Syst 10(2):144–154

    Article  Google Scholar 

  • Subramanian K, Suresh S (2012a) Human action recognition using meta-cognitive neuro-fuzzy inference system. Int J Neural Sys 22(6):1250028 (15)

    Google Scholar 

  • Subramanian K, Suresh S (2012b) A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system. Appl Soft Comput 12(11):3603–3614

    Article  Google Scholar 

  • Subramanian K, Suresh S (2013) A projection based learning algorithm for Meta-Cognitive Neuro-Fuzzy Inference system. In: Fuzzy systems (FUZZ), 2013 IEEE International Conference, pp 1–8

  • Subramanian K, Savitha R, Suresh S (2012a) Complex-valued neuro-fuzzy inference system for wind prediction. In: International Joint Conference on Neural Networks(IJCNN), Brisbane, pp 1–7

  • Subramanian K, Savitha R, Suresh S, Mahanand B (2012b) Complex-valued neuro-fuzzy inference system based classifier. In: Swarm, evolutionary, and memetic computing. Springer, Berlin Heidelberg, pp 348–355

  • Subramanian K, Savitha R, Suresh S (2013a) A meta-cognitive interval type-2 fuzzy inference system classifier and its projection based learning algorithm. In: Evolving and adaptive intelligent systems (EAIS), 2013 IEEE Conference, pp 48–55

  • Subramanian K, Suresh S, Sundararajan N (2013b) A metacognitive neuro-fuzzy inference system (mcfis) for sequential classification problems. Fuzzy Syst IEEE Trans 21(6):1080–1095

    Article  Google Scholar 

  • Subramanian K, Savitha R, Suresh S (2014) A complex-valued neuro-fuzzy inference system and its learning mechanism. Neurocomputing 123:110–120

    Article  Google Scholar 

  • Suresh S, Babu RV, Kim HJ (2009) No-reference image quality assessment using modified extreme learning machine classifier. Appl Soft Comput 9(2):541–552

    Article  Google Scholar 

  • Suresh S, Dong K, Kim H (2010) A sequential learning algorithm for self-adaptive resource allocation network classifier. Neurocomputing 73(16–18):3012–3019

    Article  Google Scholar 

  • Suresh S, Savitha R, Sundararajan N (2011) A sequential learning algorithm for complex valued self regulating resource allocation network- CSRAN. IEEE Trans Neural Netw 22(7):1061–1072

    Article  Google Scholar 

  • Suresh S, Sundararajan N, Saratchandran P (2008) Risk sensitive loss functions for sparse multi-category classification problems. Inf Sci 179(21):2621–2638

    Article  MathSciNet  Google Scholar 

  • Tavoosi J, Badamchizadeh MA (2012) A class of type-2 fuzzy neural networks for nonlinear dynamical system identification. Neural Comput Appl 23(3–4):707–717

    Google Scholar 

  • Vazquez DM, Rubio JJ, Pacheco J (2012) Characterisation framework for epileptic signals. Image Process IET 6(9):1227–1235

    Article  MathSciNet  Google Scholar 

  • Wu D (2012) An overview of alternative type-reduction approaches for reducing the computational cost of interval type-2 fuzzy logic controllers. In: IEEE Intl. Conf. on Fuzzy Systems, pp 1–8

  • Zhang T (2003) Statistical behavior and consistency of classification methods based on convex risk minimization. Ann Stat 32(1):56–85

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank Dr. B. S. Mahanand for providing the features for ADHD data set.

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Authors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Kartick Subramanian, Ankit Kumar Das, Suresh Sundaram & Savitha Ramasamy

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  1. Kartick Subramanian
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Correspondence to Kartick Subramanian.

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Extended version of manuscript submitted to IEEE International Conference on Evolving and Adaptive Intelligent System, 2014.

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Subramanian, K., Das, A.K., Sundaram, S. et al. A meta-cognitive interval type-2 fuzzy inference system and its projection based learning algorithm. Evolving Systems 5, 219–230 (2014). https://doi.org/10.1007/s12530-013-9102-9

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