Bankruptcy forecasting: A hybrid approach using Fuzzy c-means clustering and Multivariate Adaptive Regression Splines (MARS)

Andrés, Javier De; Lorca, Pedro Tedde de; Juez, Francisco Javier de Cos; Sánchez-Lasheras, Fernando

doi:10.1016/j.eswa.2010.07.117

Cited by 144 publications

(46 citation statements)

References 64 publications

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“…• The problem domains tackled have been breast cancer [34], credit score forecasting [35], prediction of properties in steel strips [44], bankruptcy forecasting [45], sales prediction [46], and forecasting of customer dissatisfaction and turnover [48]. Regarding the domain of software project prediction, we found two studies that use MARS along with other techniques to find the best method for predicting software effort [49,50].…”

Section: Studies On Multivariate Adaptive Regression Splines (Mars)mentioning

confidence: 98%

“…• Some studies compared the performance of MARS with that of linear discriminant analysis [34,35,45], neural networks [34, 35, 44-46, 49, 50], support vector machines [35,46,49,50], tree-based approaches [35,49,50], linear regression [46,49,50], logistic regression [35,48], and ARIMA [46]. • Two studies concluded that combining MARS with another technique outperformed the rest of the methods, such as MARS with a neural network [34] and a hybrid fuzzy clustering-MARS [45]. A stand-alone MARS outperformed in some studies such as [35,44,46].…”

Section: Studies On Multivariate Adaptive Regression Splines (Mars)mentioning

confidence: 99%

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Metaheuristic optimization of multivariate adaptive regression splines for predicting the schedule of software projects

Ferreira-Santiago

López-Martín

Yáñez-Márquez

2015

Neural Comput & Applic

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A qualitative common perception of the software industry is that it finishes its projects late and over budget, whereas from a quantitative point of view, only 39 % of software projects are finished on time compared to the schedule when the project started. This low percentage has been attributed to factors such as unrealistic time frames and lack of planning regarding poor prediction. The main techniques used for predicting project schedule have mainly been based on expert judgment and mathematical models. In this study, a new model, derived from the multivariate adaptive regression splines (MARS) model, is proposed. This new model, optimized MARS (OMARS), uses a simulated annealing process to find a transformation of the input data space prior to applying MARS in order to improve accuracy when predicting the schedule of software projects. The prediction accuracy of the OMARS model is compared to that of stand-alone MARS and a multiple linear regression (MLR) model with a logarithmic transformation. The two independent variables used for training and testing the models are functional size, which corresponds to a composite value of 19 independent variables, and the maximum size of the team of developers. The data set of projects was obtained from the International Software Benchmarking Standards Group (ISBSG) Release 11. Results based on the absolute residuals and t paired and Wilcoxon statistical tests showed that prediction accuracy with OMARS is statistically better than that with the MARS and MLR models.

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Section: Studies On Multivariate Adaptive Regression Splines (Mars)mentioning

confidence: 98%

Section: Studies On Multivariate Adaptive Regression Splines (Mars)mentioning

confidence: 99%

Metaheuristic optimization of multivariate adaptive regression splines for predicting the schedule of software projects

Ferreira-Santiago

López-Martín

Yáñez-Márquez

2015

Neural Comput & Applic

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“…Multivariate Adaptive Regression Splines (MARSplines), being a generalized form of regression model, aim to create a model for the prediction of dependent variable based on the volatility of explanatory variables [29][30][31][32][33]. For the analysis of MARSplines, it is not necessary to have a priori knowledge about the form of the relationship between variables.…”

Section: Regression and Marsplinesmentioning

confidence: 99%

Data mining tools in identifying the components of the microstructure of compacted graphite iron based on the content of alloying elements

Wilk-Kołodziejczyk

Regulski

Gumienny

et al. 2017

Int J Adv Manuf Technol

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The application of data mining techniques in the design of modern foundry materials allows achieving higher product quality indicators. Designing of a new product always requires thorough knowledge of the effect of alloying elements on the microstructure and hence also on the properties of the examined material. The conducted experimental studies allow for a qualitative assessment of the indicated relationships, but it is the use of intelligent computational techniques that enables building an approximation model of the microstructure and, owing to this, make predictions with high precision. The developed model of prediction supports the technology-related decisions as early as at the stage of casting design and is considered the first step in selecting the type of material used.

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“…In these models the first stage of calculating training set, using a suitable algorithm discriminatory type of method k -medium [8], c -medium [9], self-organizing neural networks [10], is divided into appropriate classes, within which they are made predictions based on independent models. In many cases [11,12], this approach can improve the ability of predictive mathematical model, but in a situation of extremely diverse number of data in each class or insufficient input data there may be problems at the stage of learning and testing statistical models.…”

Section: Introductionmentioning

confidence: 99%

Application of Selected Methods of Black Box for Modelling the Settleability Process in Wastewater Treatment Plant

Szeląg

Gawdzik

2017

Ecological Chemistry and Engineering S

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Abstract:The paper described how the results of measurements of inflow wastewater temperature in the chamber, a degree of external and internal recirculation in the biological-mechanical wastewater treatment plant (WWTP) in Cedzyna near Kielce, Poland, were used to make predictions of settleability of activated sludge. Three methods, namely: multivariate adaptive regression splines (MARS), random forests (RF) and modified random forests (RF + SOM) were employed to compute activated sludge settleability. The results of analysis indicate that modified random forests demonstrate the best predictive abilities.

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Bankruptcy forecasting: A hybrid approach using Fuzzy c-means clustering and Multivariate Adaptive Regression Splines (MARS)

Cited by 144 publications

References 64 publications

Metaheuristic optimization of multivariate adaptive regression splines for predicting the schedule of software projects

Metaheuristic optimization of multivariate adaptive regression splines for predicting the schedule of software projects

Data mining tools in identifying the components of the microstructure of compacted graphite iron based on the content of alloying elements

Application of Selected Methods of Black Box for Modelling the Settleability Process in Wastewater Treatment Plant

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