RETRACTED ARTICLE: Intelligent hybrid model for financial crisis prediction using machine learning techniques

Uthayakumar, J.; Metawa, Noura; Shankar, K.; Lakshmanaprabu, S.K.

doi:10.1007/s10257-018-0388-9

Cited by 61 publications

(40 citation statements)

References 29 publications

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“…26 The presented ACO algorithm in-volves the ACO algorithm for both FS and classification. In the study by Uthayakumar et al 27 , a novel cluster-based classifier model for FCP has been presented, which involves improved K-means-based clustering and a fitness-scaling chaotic genetic ant colony algorithm (FSCGACA)-based classifier model. Initially, the improved K-means clustering technique discards the misclustered data.…”

Section: Related Workmentioning

confidence: 99%

Computational Intelligence-Based Financial Crisis Prediction Model Using Feature Subset Selection with Optimal Deep Belief Network

et al. 2021

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At present times, financial decisions are mainly based on the classifier technique, which is utilized to allocate a collection of observations into fixed groups. A diverse set of data classifier approaches were presented for forecasting the financial crisis of an institution using the past data. An essential process toward the design of a precise financial crisis prediction (FCP) approach comprises the choice of proper variables (features) that are related to the issues at hand. This is termed as a feature selection (FS) issue that assists to improvise the classifier results. Besides, computational intelligence techniques can be used as a classification model to determine the financial crisis of an organization. In this view, this article introduces a new FS using elephant herd optimization (EHO) with modified water wave optimization (MWWO) algorithm-based deep belief network (DBN) for FCP. The EHO algorithm is applied as a feature selector, and MWWO-DBN is utilized for the classification process. The application of the MWWO algorithm helps to tune the parameters of the DBN model, and the choice of optimal feature subset from the EHO algorithm leads to enhanced classification performance. The experimental results of the proposed model are tested against three benchmark data sets, namely AnalcatData, German Credit, and Australian Credit. The obtained simulation results indicated the superior performance of the proposed model by attaining maximum classification performance.

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Section: Related Workmentioning

confidence: 99%

Computational Intelligence-Based Financial Crisis Prediction Model Using Feature Subset Selection with Optimal Deep Belief Network

et al. 2021

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“…al. (2018) [43] Ant [44] Hybrid logistic regression-naive bayes (90%-10%)) 99.64 Nanxi Wang (2017) [45] Neural network model with robust logistic regression (50%-50%) 69.44 Nanxi Wang (2017) [45] Neural network model with inductive learning algorithm (50%-50%) 89.7 Nanxi Wang (2017) [45] Neural network model with genetic algorithm (50%-50%) 94 Nanxi Wang (2017) [45] Neural network model with neural networks without dropout (50%-50%) 90.3 Nanxi Wang (2017) [45] Neural network model with SVM (50%-50%) 98.67 Nanxi Wang (2017) [45] Neural network model with decision tree (50%-50%) 99.33 J. Uthayakumar et.…”

Section: Author (Year)mentioning

confidence: 99%

“…al. (2018) [43] Fitness Figure 6. This figure shows that the EPNN achieves the highest convergence rate compared to the PNN and MLP.…”

Section: Author (Year)mentioning

confidence: 99%

A Differential Evolution‐Oriented Pruning Neural Network Model for Bankruptcy Prediction

Tang

Zhu

et al. 2019

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Financial bankruptcy prediction is crucial for financial institutions in assessing the financial health of companies and individuals. Such work is necessary for financial institutions to establish effective prediction models to make appropriate lending decisions. In recent decades, various bankruptcy prediction models have been developed for academics and practitioners to predict the likelihood that a loan customer will go bankrupt. Among them, Artificial Neural Networks (ANNs) have been widely and effectively applied in bankruptcy prediction. Inspired by the mechanism of biological neurons, we propose an evolutionary pruning neural network (EPNN) model to conduct financial bankruptcy analysis. The EPNN possesses a dynamic dendritic structure that is trained by a global optimization learning algorithm: the Adaptive Differential Evolution algorithm with Optional External Archive (JADE). The EPNN can reduce the computational complexity by removing the superfluous and ineffective synapses and dendrites in the structure and is simultaneously able to achieve a competitive classification accuracy. After simplifying the structure, the EPNN can be entirely replaced by a logic circuit containing the comparators and the logic NOT, AND, and OR gates. This mechanism makes it feasible to apply the EPNN to bankruptcy analysis in hardware implementations. To verify the effectiveness of the EPNN, we adopt two benchmark datasets in our experiments. The experimental results reveal that the EPNN outperforms the Multilayer Perceptron (MLP) model and our previously developed preliminary pruning neural network (PNN) model in terms of accuracy, convergence speed, and Area Under the Receiver Operating Characteristics (ROC) curve (AUC). In addition, the EPNN also provides competitive and satisfactory classification performances in contrast with other commonly used classification methods.

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“…Presently, the development of statistical tools, artificial intelligence (AI), machine learning (ML), and evolutionary algorithms (EA) have resulted in the design of accurate and efficient STLF models [4,5]. These technologies utilize intelligent and adaptive components which necessitate recent techniques for precise generation and demand prediction in an optimal way.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Machine Learning With Evolutionary Algorithm Based Short Term Load Forecasting in Power Systems

et al. 2021

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Electricity demand forecasting remains a challenging issue for power system scheduling at varying stages of energy sectors. Short Term load forecasting (STLF) plays a vital part in regulated power systems and electricity markets, which is commonly employed to predict the outcomes power failures. This paper presents an intelligent machine learning with evolutionary algorithm based STLF model, called (IMLEA-STLF) for power systems which involves different stages of operations such as data decomposition, data preprocessing, feature selection, prediction, and parameter tuning. Wavelet transform (WT) is used for the decomposition of the time series and Oppositional Artificial Fish Swarm Optimization algorithm (OAFSA) based feature selection technique to elect an optimal set of features. In order to improvise the convergence rate of AFSA, oppositional based learning (OBL) concept is integrated into it. Then, the water wave optimization (WWO) with Elman neural networks (ENN) model is employed for the predictive process. Finally, inverse WT is applied and obtained the hourly load forecasting data. To validate the effective predictive outcome of the IMLEA-STLF model, an extensive set of simulations take place on benchmark dataset. The resultant values ensured the promising results of the IMLEA-STLF model over the other compared methods.

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RETRACTED ARTICLE: Intelligent hybrid model for financial crisis prediction using machine learning techniques

Cited by 61 publications

References 29 publications

Computational Intelligence-Based Financial Crisis Prediction Model Using Feature Subset Selection with Optimal Deep Belief Network

Computational Intelligence-Based Financial Crisis Prediction Model Using Feature Subset Selection with Optimal Deep Belief Network

A Differential Evolution‐Oriented Pruning Neural Network Model for Bankruptcy Prediction

Intelligent Machine Learning With Evolutionary Algorithm Based Short Term Load Forecasting in Power Systems

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