Risk Classification in Global Software Development Using a Machine Learning Approach

Iftikhar, Asim; Musa, Shahrulniza; Alam, Muhammad Mansoor; Ahmed, Rizwan; Su’ud, Mazliham Mohd; Khan, Laiq Muhammad; Ali, Syed Mubashir

doi:10.4018/jitr.299385

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…The latter has similarities with this paper by aligning in vision and differing through the approach of discrete segment formation. No less important is to stress that papers such as Silva's [17], Raymaekers's [47] and Iftikhar's [48,49] highlight the importance of the resulting classifiers' explainability in multiple industries and cases of use, which is one of the purposes of this paper.…”

Section: Related Workmentioning

confidence: 99%

Combination of unsupervised discretization methods for credit risk

Fuentes Cabrera,

Pérez Vicente,

Maldonado

et al. 2023

PLoS ONE

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Creating robust and explainable statistical learning models is essential in credit risk management. For this purpose, equally spaced or frequent discretization is the de facto choice when building predictive models. The methods above have limitations, given that when the discretization procedure is constrained, the underlying patterns are lost. This study introduces an innovative approach by combining traditional discretization techniques with clustering-based discretization, specifically k means and Gaussian mixture models. The study proposes two combinations: Discrete Competitive Combination (DCC) and Discrete Exhaustive Combination (DEC). Discrete Competitive Combination selects features based on the discretization method that performs better on each feature, whereas Discrete Exhaustive Combination includes every discretization method to complement the information not captured by each technique. The proposed combinations were tested on 11 different credit risk datasets by fitting a logistic regression model using the weight of evidence transformation over the training partition and contrasted over the validation partition. The experimental findings showed that both combinations similarly outperform individual methods for the logistic regression without compromising the computational efficiency. More importantly, the proposed method is a feasible and competitive alternative to conventional methods without reducing explainability.

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Section: Related Workmentioning

confidence: 99%

Combination of unsupervised discretization methods for credit risk

Fuentes Cabrera,

Pérez Vicente,

Maldonado

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Machine learning and deep learning models have been employed in several domains including medical image analysis, object detection, data mining, risk analysis, etc. For example, [21] used machine learning models like SVM and K-NN risks related to global software development projects. Similarly, [22] performs risk prediction related to time, cost, and resources.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Prediction of Students in Higher Education Using Multi-Model Ensemble Approach

Butt,

Mahmood,

Shakeel

et al. 2023

IEEE Access

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Many stakeholders including students, teachers, and educational institutions, benefit from accurately predicting student performance and facilitating data-driven policies. In this field, providing users with accurate and understandable predictions is challenging, but equally important. The goals of this study are multifaceted: to identify students at-risk; to identify differences in assessment across different environments; methods for assessing students; and to determine the relationship between teacher employment status and student achievement. This study performs an empirical comparison of the performance and efficiency of ensemble classification methods based on bagging, boosting, stacking, and voting for successful predictions. An ensemble model is developed and validated using double, triple, and quadruple combinations of classification algorithms using Naive Bayes, J48 decision trees, Adaboost, logistics, and multilayer perceptron. This study uses primary quantitative data from the learning management system of a university in Pakistan to analyze the performance of these models. The boosted tree detection method outperforms bagged trees when the standard deviation is higher and the data size is large, while stacking is best for smaller datasets. Based on behavioral analysis results of students, academic advice can be given for selected case studies. These will help educational administrators and policymakers working in education to introduce new policies and curricula accordingly.

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Risk Classification in Global Software Development Using a Machine Learning Approach

Cited by 2 publications

References 34 publications

Combination of unsupervised discretization methods for credit risk

Combination of unsupervised discretization methods for credit risk

Performance Prediction of Students in Higher Education Using Multi-Model Ensemble Approach

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