A hybrid meta-cognitive restricted boltzmann machine classifier for credit scoring

Meta-cognition with self-regulated learning equips a machine learning algorithm to make judicious decisions about every sample in the training data set. Due to this capability, meta-cognitive machine learning algorithms exhibit better generalization behavior. In the past, numerous works have focused on studying the effect on meta-cognition for learning patterns from data in a supervised fashion. In this paper, we attempt to study the effect of meta-cognition towards efficient feature representation in a Restricted Boltzmann Machine (RBM). To this end, we develop a Meta-cognitive Restricted Boltzmann Machine (McRBM) that decides what-to-represent, how-to-represent and when-to-represent for each sample in the training data set through its Sample deletion, Feature Representation and Sample Reserve strategies, respectively. The meta-cognitive component of McRBM helps to improve the generative training of RBM. The features thus generated by RBM are then used in classification through back propagation learning, which is derived based on the hinge-loss error function. The McRBM is used to solve credit scoring problem using the German credit data set, Australian credit data set and the KAGGLE credit data set. Performance of McRBM is compared against the Support Vector Machines (SVM), Extreme Learning Machines (ELM), Multi-layer Perceptron with Back Propagation (MLP-BP), and Classification Restricted Boltzmann Machine (ClassRBM). Performance results show superior classification ability of McRBM.

[1]  Sundaram Suresh,et al.  Sequential Projection-Based Metacognitive Learning in a Radial Basis Function Network for Classification Problems , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[2]  Raquel Flórez López,et al.  Enhancing accuracy and interpretability of ensemble strategies in credit risk assessment. A correlated-adjusted decision forest proposal , 2015, Expert Syst. Appl..

[3]  Narasimhan Sundararajan,et al.  Risk-sensitive loss functions for sparse multi-category classification problems , 2008, Inf. Sci..

[4]  J. Suykens,et al.  Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research , 2015, Eur. J. Oper. Res..

[5]  Sundaram Suresh,et al.  Metacognitive Learning in a Fully Complex-Valued Radial Basis Function Neural Network , 2012, Neural Computation.

[6]  Sotirios Chatzis,et al.  A novel corporate credit rating system based on Student's-t hidden Markov models , 2016, Expert Syst. Appl..

[7]  Darsana P. Josyula,et al.  Modeling metacognition for learning in artificial systems , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[8]  Geoffrey E. Hinton.  Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[9]  Sirong Luo,et al.  Spline based survival model for credit risk modeling , 2016, Eur. J. Oper. Res..

[10]  Maysam F. Abbod,et al.  Classifiers consensus system approach for credit scoring , 2016, Knowl. Based Syst..

[11]  Jakub M. Tomczak,et al.  Classification Restricted Boltzmann Machine for comprehensible credit scoring model , 2015, Expert Syst. Appl..

[12]  Razvan Pascanu,et al.  Learning Algorithms for the Classification Restricted Boltzmann Machine , 2012, J. Mach. Learn. Res..

[13]  Vural Aksakalli,et al.  Risk assessment in social lending via random forests , 2015, Expert Syst. Appl..

[14]  Sundaram Suresh,et al.  A meta-cognitive interval type-2 fuzzy inference system and its projection based learning algorithm , 2014, Evol. Syst..

[15]  So Young Sohn,et al.  Technology credit scoring model with fuzzy logistic regression , 2016, Appl. Soft Comput..

[16]  Sundaram Suresh,et al.  A projection based learning in Meta-cognitive Radial Basis Function Network for classification problems , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[17]  Christopher J. Merz,et al.  UCI Repository of Machine Learning Databases , 1996 .

[18]  Sundaram Suresh,et al.  Meta-cognitive Neural Network for classification problems in a sequential learning framework , 2012, Neurocomputing.

[19]  Sundaram Suresh,et al.  A Meta-Cognitive Learning Algorithm for an Extreme Learning Machine Classifier , 2013, Cognitive Computation.

[20]  Yu Wang,et al.  Ensemble classification based on supervised clustering for credit scoring , 2016, Appl. Soft Comput..

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

[22]  Sundaram Suresh,et al.  A Metacognitive Complex-Valued Interval Type-2 Fuzzy Inference System , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[23]  Johan A. K. Suykens,et al.  Benchmarking state-of-the-art classification algorithms for credit scoring , 2003, J. Oper. Res. Soc..

[24]  Sundaram Suresh,et al.  A meta-cognitive learning algorithm for a Fully Complex-valued Relaxation Network , 2012, Neural Networks.