ROSE: a Package for Binary Imbalanced Learning

Lunardon, Nicola; Menardi, Giovanna; Torelli, Nicola

doi:10.32614/rj-2014-008

Cited by 385 publications

(240 citation statements)

References 4 publications

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“…Further details on how to select the smoothing matrix

H_{j}

can be found in Menardi and Torelli () and Lunardon et al . ().…”

Section: Simulation Based On the Taiwan Credit Card Data Setsupporting

confidence: 92%

“…In particular, we ‘balanced’ the data by using random oversampling examples (ROSE, by Menardi and Torelli () and Lunardon et al . ()) and the synthetic minority oversampling technique SMOTE (by Chawla et al . ()) methods for various EPV‐values and develop prediction models by using logistic regression.…”

Section: Introductionmentioning

confidence: 99%

“…Since statistical models for rare events are rarely used in this framework, we compared Firth's (1993) method, a modification of Firth's logistic regression called Firth's logistic regression with added covariate (FLAC, proposed by Puhr et al (2017)) and logistic regression penalized by a log-F prior (LF22, by Greenland and Mansournia (2015)) with methods such as linear discriminant analysis (LDA), ridge regression (Ridge), the GEV model and logistic regression. In particular, we 'balanced' the data by using random oversampling examples (ROSE, by Menardi and Torelli (2014) and Lunardon et al (2014)) and the synthetic minority oversampling technique SMOTE (by Chawla et al (2002)) methods for various EPV-values and develop prediction models by using logistic regression. Since the main reason for modelling PD is to predict loan applicants who have the tendency to default, we focus on model evaluation criteria for predictions in the context of regression analysis rather than classification.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Default Probability by using Statistical Models for rare Events

Ogundimu

2019

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary Prediction models in credit scoring usually involve the use of data sets with highly imbalanced distributions of the event of interest (default). Logistic regression, which is widely used to estimate the probability of default, PD, often suffers from the problem of separation when the event of interest is rare and consequently poor predictive performance of the minority class in small samples. A common solution is to discard majority class examples, to duplicate minority class examples or to use a combination of both to balance the data. These methods may overfit data. It is unclear how penalized regression models such as Firth's estimator, which reduces bias and mean‐square error relative to classical logistic regression, performs in modelling PD. We review some methods for class imbalanced data and compare them in a simulation study using the Taiwan credit card data. We emphasize the effect of events per variable for developing an accurate model—an often neglected concept in PD‐modelling. The data balancing techniques that are considered are the random oversampling examples and synthetic minority oversampling technique methods. The results indicate that the synthetic minority oversampling technique improved predictive accuracy of PD regardless of sample size. Among the penalized regression models that are analysed, the log‐F prior and ridge regression methods are preferred.

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“…Further details on how to select the smoothing matrix

H_{j}

can be found in Menardi and Torelli () and Lunardon et al . ().…”

Section: Simulation Based On the Taiwan Credit Card Data Setsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Default Probability by using Statistical Models for rare Events

Ogundimu

2019

Journal of the Royal Statistical Society Series A: Statistics in Society

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“…In order to address the problem of class imbalance, two remedial measures will be applied in the case studies. More specifically, SMOTE and ROSE sampling (Lunardon et al, 2014) can be selected to create a more balanced training dataset. In short, SMOTE draws artificial samples by choosing points that lie on the line connecting the rare observation to one of its nearest neighbours in the feature space.…”

Section: Intrinsic Properties Of Machine Learning Techniquesmentioning

confidence: 99%

Machine learning techniques for the automation of literature reviews and systematic reviews in EFSA

Jaspers

Troyer

Aerts

2018

EFS3

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This Report presents the results from EFSA project RC/EFSA/AMU/2016/01 related to the implementation of machine learning techniques for literature reviews and systematic reviews in EFSA. An overview of the different steps of a systematic review is provided, along with possible ways for automation. Although it was found that most steps could benefit from automation, it was also observed that some steps require more sophisticated methods than those encompassed within the machine learning framework. Availability of data and methodology allowed for the development of an automatic screening tool based on several machine learning techniques. The developed shiny R application can be used for the screening of abstracts and full texts. Properties of machine learning techniques are discussed in this Report together with their most important advantages and disadvantages. The latter discussion includes both general properties, as well as context-specific properties based on their performance in three case studies. Although creating a universal automatic data extraction tool was considered to be infeasible in this stage, this step of the systematic review was addressed to allow the reviewer to scan the uploaded pdf files for certain words or string of words. Based on observations from the performed case studies, recommendations were made regarding which methods are preferred in specific situations. More explicitly, a discussion is made about the performance of the classifiers with respect to the magnitude of the pool of papers to be screened as well as to the amount of imbalance, referring to the proportion of relevant and irrelevant papers. Finally, it was concluded that the results presented in this report provide proof that the developed shiny application could be efficiently used in combination with other software such as DistillerSR. © European Food Safety Authority, 2018Key words: Systematic Reviews, Machine Learning, screening, data extraction, Sensitivity, Specificity Disclaimer: The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors. Reproduction is authorised provided the source is acknowledged. Machine Learning Techniques for Literature and Systematic Reviewswww.efsa.europa.eu/publications 3 EFSA Supporting publication 2018:EN-1427The present document has been produced and adopted by the bodies identified above as author. This task has been carried out exclusively by the author in the context of a contract ...

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“…We aimed to collect about 25 cases of interest within each participant. Since this data was still comprised of substantially more non-lapse than lapse cases (unbalanced data), non-lapse cases were randomly sampled in a 1:1 ratio to lapse cases via the ROSE package in the R statistical computing software (e.g., see [49, 50]). This 1:1 sampling resulted in oversampling of the lapse-cases and undersampling of the non-lapse cases in order to create a balanced data set, which is suggested for optimal classification accuracy in these types of models [51].…”

Section: Problems and Future Considerationsmentioning

confidence: 99%

Return of the JITAI: Applying a Just-in-Time Adaptive Intervention Framework to the Development of m-Health Solutions for Addictive Behaviors

et al. 2017

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Purpose Lapses are strong indicators of later relapse among individuals with addictive disorders, and thus are an important intervention target. However, lapse behavior has proven resistant to change due to the complex interplay of lapse triggers that are present in everyday life. It could be possible to prevent lapses before they occur by using m-Health solutions to deliver interventions in real-time. Method Just-in-time adaptive intervention (JITAI) is an intervention design framework that could be delivered via mobile app to facilitate in-the-moment monitoring of triggers for lapsing, and deliver personalized coping strategies to the user to prevent lapses from occurring. An organized framework is key for successful development of a JITAI. Results Nahum-Shani and colleagues (2014) set forth six core elements of a JITAI and guidelines for designing each: distal outcomes, proximal outcomes, tailoring variables, decision points, decision rules, and intervention options. The primary aim of this paper is to illustrate the use of this framework as it pertains to developing a JITAI that targets lapse behavior among individuals following a weight control diet. Conclusion We will detail our approach to various decision points during the development phases, report on preliminary findings where applicable, identify problems that arose during development, and provide recommendations for researchers who are currently undertaking their own JITAI development efforts. Issues such as missing data, the rarity of lapses, advantages/disadvantages of machine learning, and user engagement are discussed.

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ROSE: a Package for Binary Imbalanced Learning

Cited by 385 publications

References 4 publications

Prediction of Default Probability by using Statistical Models for rare Events

Prediction of Default Probability by using Statistical Models for rare Events

Machine learning techniques for the automation of literature reviews and systematic reviews in EFSA

Return of the JITAI: Applying a Just-in-Time Adaptive Intervention Framework to the Development of m-Health Solutions for Addictive Behaviors

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