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Rather A.M.,University of Hyderabad | Agarwal A.,University of Hyderabad | Sastry V.N.,Banking Technology
Expert Systems with Applications | Year: 2015

In this paper, we propose a robust and novel hybrid model for prediction of stock returns. The proposed model is constituted of two linear models: autoregressive moving average model, exponential smoothing model and a non-linear model: recurrent neural network. Training data for recurrent neural network is generated by a new regression model. Recurrent neural network produces satisfactory predictions as compared to linear models. With the goal to further improve the accuracy of predictions, the proposed hybrid prediction model merges predictions obtained from these three prediction based models. An optimization model is introduced which generates optimal weights for proposed model; the model is solved using genetic algorithms. The results confirm about the accuracy of the prediction performance of recurrent neural network. As expected, an outstanding prediction performance has been obtained from proposed hybrid prediction model as it outperforms recurrent neural network. The proposed model is certainly expected to be a promising approach in the field of prediction based models where data is non-linear, whose patterns are difficult to be captured by traditional models. © 2014 Elsevier Ltd. Source


Mohanty R.,Keshav Memorial Institute of Technology | Ravi V.,Banking Technology | Patra M.R.,Berhampur University
Applied Soft Computing Journal | Year: 2013

In this paper, we propose novel recurrent architectures for Genetic Programming (GP) and Group Method of Data Handling (GMDH) to predict software reliability. The effectiveness of the models is compared with that of well-known machine learning techniques viz. Multiple Linear Regression (MLR), Multivariate Adaptive Regression Splines (MARS), Backpropagation Neural Network (BPNN), Counter Propagation Neural Network (CPNN), Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS), TreeNet, GMDH and GP on three datasets taken from literature. Further, we extended our research by developing GP and GMDH based ensemble models to predict software reliability. In the ensemble models, we considered GP and GMDH as constituent models and chose GP, GMDH, BPNN and Average as arbitrators. The results obtained from our experiments indicate that the new recurrent architecture for GP and the ensemble based on GP outperformed all other techniques. © 2012 Elsevier B.V. Source


Ravisankar P.,Banking Technology | Ravi V.,Banking Technology | Bose I.,University of Hong Kong
Information Sciences | Year: 2010

This paper presents novel neural network-genetic programming hybrids to predict the failure of dotcom companies. These hybrids comprise multilayer feed forward neural network (MLFF), probabilistic neural network (PNN), rough sets (RS) and genetic programming (GP) in a two-phase architecture. In each hybrid, one technique is used to perform feature selection in the first phase and another one is used as a classifier in the second phase. Further t-statistic and f-statistic are also used separately for feature selection in the first phase. In each of these cases, top 10 features are selected and fed to the classifier. Also, the NN-GP hybrids are compared with MLFF, PNN and GP in their stand-alone mode without feature selection. The dataset analyzed here is collected from Wharton Research Data Services (WRDS). It consists of 240 dotcom companies of which 120 are failed and 120 are healthy. Ten-fold cross-validation is performed throughout the study. Results in terms of average accuracy, average sensitivity, average specificity and area under the receiver operating characteristic curve (AUC) indicate that the GP outperformed all the techniques with or without feature selection. The superiority of GP-GP is demonstrated by t-test at 10% level of significance. Furthermore, the results are much better than those reported in previous studies on the same dataset. © 2009 Elsevier Inc. All rights reserved. Source


Ravisankar P.,Banking Technology | Ravi V.,Banking Technology
Knowledge-Based Systems | Year: 2010

This paper presents three hitherto unused neural network architectures for bankruptcy prediction in banks. These networks are Group Method of Data Handling (GMDH), Counter Propagation Neural Network (CPNN) and fuzzy Adaptive Resonance Theory Map (fuzzy ARTMAP). Efficacy of each of these techniques is tested by using four different datasets pertaining to Spanish banks, Turkish banks, UK banks and US banks. Further t-statistic, f-statistic and GMDH are used for feature selection purpose and the features so selected are fed as input to GMDH, CPNN and fuzzy ARTMAP for classification purpose. In each of these cases, top five features are selected in the case of Spanish dataset and top seven features are selected in the case of Turkish and UK datasets. It is observed that the features selected by t-statistic and f-statistic are identical in all datasets. Further, there is a good overlap in the features selected by t-statistic and GMDH. The performance of these hybrids is compared with that of GMDH, CPNN and fuzzy ARTMAP in their stand-alone mode without feature selection. Ten-fold cross validation is performed throughout the study. Results indicate that the GMDH outperformed all the techniques with or without feature selection. Furthermore, the results are much better than those reported in previous studies on the same datasets in terms of average accuracy, average sensitivity and average specificity. © 2010 Elsevier B.V. All rights reserved. Source


Reddy K.N.,Banking Technology | Ravi V.,Banking Technology
Knowledge-Based Systems | Year: 2013

In this paper, two novel kernel based soft computing techniques viz., Differential Evolution trained Kernel Principal Component Wavelet Neural Network (DE-KPCWNN) and DE trained Kernel Binary Quantile Regression (DE-KBQR) are proposed for classification. While, the former can solve multi-class classification problems, the latter can solve binary classification problems only. In the proposed DE-KPCWNN technique, Kernel Principal Component Analysis (KPCA) is applied to input data to get Kernel Principal Components, on which we will employ WNN. Then, DE is used to train the resulting KPCWNN. In DE-KBQR we applied Kernel technique on the input data to get Kernel Matrix, on which we will employ BQR. Then, DE is used to train the resulting KBQR. Several experiments are conducted on four bankruptcy datasets, three benchmark datasets and two Credit scoring datasets to assess the effectiveness of the proposed classification techniques. The results indicate that the proposed Soft Computing hybrids for classification are efficient than the existing classification techniques. Out of the two, DE-KBQR performed relatively better compared to DE-KPCWNN on a majority of binary classification problems. This is the significant outcome of this study. © 2012 Elsevier B.V. All rights reserved. Source

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