A comparative study on base classifiers in ensemble methods for credit scoring

Koklu

et al. 2017

ijisae

Abstract:The definition of the data mining can be told as to extract information or knowledge from large volumes of data. Statistical and machine learning techniques are used for the determination of the models to be used for data mining predictions. Today, data mining is used in many different areas such as science and engineering, health, commerce, shopping, banking and finance, education and internet. This study make use of WEKA (Waikato Environment for Knowledge Analysis) to compare the different classification techniques on energy efficiency datasets. In this study 10 different Data Mining methods namely Bagging, Decorate, Rotation Forest, J48, NNge, K-Star, Naïve Bayes, Dagging, Bayes Net and JRip classification methods were applied on energy efficiency dataset that were taken from UCI Machine Learning Repository. When comparing the performances of algorithms it's been found that Rotation Forest has highest accuracy whereas Dagging had the worst accuracy.

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Comparison of Classification Techniques on Energy Efficiency Dataset

Koklu

et al. 2017

ijisae

“…A bagging scheme that uses a type of credal tree different from the CDT presented in [15] will be described in this work. This new model achieves better results than the bagging of CDT shown in [20] when data sets with added noise are classified.…”

Section: Introductionmentioning

confidence: 99%

“…In the last years, it has been checked that the CDT model presents good experimental results in standard classification tasks (see Abellán and Moral [18] and Abellán and Masegosa [19]). The bagging scheme, using CDT as base classifier, has been used for the particular task of classifying data sets about credit scoring (see Abellán and Castellano [20]). A bagging scheme that uses a type of credal tree different from the CDT presented in [15] will be described in this work.…”

Section: Introductionmentioning

confidence: 99%

A New Robust Classifier on Noise Domains: Bagging of Credal C4.5 Trees

2017

Self Cite

The knowledge extraction from data with noise or outliers is a complex problem in the data mining area. Normally, it is not easy to eliminate those problematic instances. To obtain information from this type of data, robust classifiers are the best option to use. One of them is the application of bagging scheme on weak single classifiers. The Credal C4.5 (CC4.5) model is a new classification tree procedure based on the classical C4.5 algorithm and imprecise probabilities. It represents a type of the so-called credal trees. It has been proven that CC4.5 is more robust to noise than C4.5 method and even than other previous credal tree models. In this paper, the performance of the CC4.5 model in bagging schemes on noisy domains is shown. An experimental study on data sets with added noise is carried out in order to compare results where bagging schemes are applied on credal trees and C4.5 procedure. As a benchmark point, the known Random Forest (RF) classification method is also used. It will be shown that the bagging ensemble using pruned credal trees outperforms the successful bagging C4.5 and RF when data sets with medium-to-high noise level are classified.

Expert Systems with Applications

“…Second, feature selection is another important data preprocessing step, and considered good practice in the domain of bankruptcy prediction (Abellán & Castellano, 2017;Tsai, 2009). Therefore, correlationbased feature selection (Hall, 2000) is applied, a basic filter feature selection approach that has seen prior applications in BFP literature (e.g.…”

Section: Asset Turnovermentioning

confidence: 99%

The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles

Bock

2017

International audienceNumerous organizations and companies rely upon business failure prediction to assess and minimize the risk of initiating business relationships with partners, clients, debtors or suppliers. Advances in research on business failure prediction have been largely dominated by algorithmic development and comparisons led by a focus on improvements in model accuracy. In this context, ensemble learning has recently emerged as a class of particularly well-performing methods, albeit often at the expense of increased model complexity. However, in practice, model choice is rarely based on predictive performance alone. Models should be comprehensible and justifiable to assess their compliance with common sense and business logic, and guarantee their acceptance throughout the organization. A promising ensemble classification algorithm that has been shown to reconcile performance and comprehensibility are rule ensembles. In this study, an extension entitled spline-rule ensembles is introduced and validated in the domain of business failure prediction. Spline-rule ensemble complement rules and linear terms found in conventional rule ensembles with smooth functions with the aim of better accommodating nonlinear simple effects of individual features on business failure. Experiments on a large selection of 21 datasets of European companies in various sectors and countries (i) demonstrate superior predictive performance of spline-rule ensembles over a set of well-established yet powerful benchmark methods, (ii) show the superiority of spline-rule ensembles over conventional rule ensembles and thus demonstrate the value of the incorporation of smoothing splines, (iii) investigate the impact of alternative term regularization procedures and (iv) illustrate the comprehensibility of the resulting models through a case study. In particular, the ability of the technique to reveal the extent and the way in which predictors impact business failure, and if and how variables interact, are exemplified