A comparative study of classifier ensembles for bankruptcy prediction

Tsai, Chih‐Fong; Hsu, Yu Feng; Yen, David C.

doi:10.1016/j.asoc.2014.08.047

Cited by 169 publications

(102 citation statements)

References 26 publications

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“…More recently, scholars have applied other machine learning methods such as random forest [72][73][74], support vector machines [75][76][77], and combinations of these approaches, obtaining increasingly accurate results. The purpose of these hybrid models is to obtain the advantages of individual models without their weaknesses [78,79].…”

Section: Lasso Regressionmentioning

confidence: 99%

Economic Sustainability in Franchising: A Model to Predict Franchisor Success or Failure

2017

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As a business model, franchising makes a major contribution to gross domestic product (GDP). A model that predicts franchisor success or failure is therefore necessary to ensure economic sustainability. In this study, such a model was developed by applying Lasso regression to a sample of franchises operating between 2002 and 2013. For franchises with the highest likelihood of survival, the franchise fees and the ratio of company-owned to franchised outlets were suited to the age of the franchise. Surviving franchises were those that opened franchised outlets at a sustainable pace, increased the franchise fee as intangible assets increased, and effectively managed profitability and efficiency.

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Section: Lasso Regressionmentioning

confidence: 99%

Economic Sustainability in Franchising: A Model to Predict Franchisor Success or Failure

2017

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“…Hung and Chen [30] proposed a selective ensemble of three classifiers (decision tree, back-propagation neural network and support vector machine) integrated with the concept of the expected probability, showing that it performs better than other stacking ensembles using the weighting or voting strategies. Tsai et al [70] carried out an extensive comparison of ensembles using MLP, support vector machines and decision trees as the base classifiers for bagging and boosting, suggesting that boosting with decision trees perform significantly better than the other ensembles.…”

Section: A Review Of Neural Network Applied To Financial Distress Prmentioning

confidence: 99%

Financial distress prediction using the hybrid associative memory with translation

Cleofas-Sánchez

García

Marqués

et al. 2016

Applied Soft Computing

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This paper presents an alternative technique for financial distress prediction systems. The method is based on a type of neural network, which is called hybrid associative memory with translation. While many different neural network architectures have successfully been used to predict credit risk and corporate failure, the power of associative memories for financial decision-making has not been explored in any depth as yet. The performance of the hybrid associative memory with translation is compared to four traditional neural networks, a support vector machine and a logistic regression model in terms of their prediction capabilities. The experimental results over nine real-life data sets show that the associative memory here proposed constitutes an appropriate solution for bankruptcy and credit risk prediction, performing significantly better than the rest of models under class imbalance and data overlapping conditions in terms of the true positive rate and the geometric mean of true positive and true negative rates.

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“…In addition, some authors have investigated the applicability of other artificial intelligence methods to bankruptcy prediction, for example, the principal component analysis [16,17], support vector machines [18,19], decision trees [20,21], rough sets, [12,22], data envelopment analysis [23,24], and others.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling bankruptcy prediction models in Slovak companies

Kováčová

Klieštiková

2017

SHS Web Conf.

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Abstract. An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression) and early artificial intelligence models (e.g. artificial neural networks), there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest) to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.

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A comparative study of classifier ensembles for bankruptcy prediction

Cited by 169 publications

References 26 publications

Economic Sustainability in Franchising: A Model to Predict Franchisor Success or Failure

Economic Sustainability in Franchising: A Model to Predict Franchisor Success or Failure

Financial distress prediction using the hybrid associative memory with translation

Modelling bankruptcy prediction models in Slovak companies

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