In this paper, we set out to compare several techniques that can be used in the analysis of imbalanced credit scoring data sets. In a credit scoring context, imbalanced data sets frequently occur as the number of defaulting loans in a portfolio is usually much lower than the number of observations that do not default. As well as using traditional classification techniques such as logistic regre...

Phishing emails are a real threat to internet communication and web economy. In real-world emails datasets, data are predominately composed of ham samples with only a small percentage of phishing ones. Standard Support Vector Machine (SVM) could produce suboptimal results in filtering phishing emails, and it often requires much time to perform the classification for large data sets. In this pap...

In this paper, we set out to compare several techniques that can be used in the analysis of imbalanced credit scoring data sets. In a credit scoring context, imbalanced data sets frequently occur as the number of defaulting loans in a portfolio is usually much lower than the number of observations that do not default. As well as using traditional classification techniques such as logistic regre...

Software defect prediction is to predict the defect-prone modules for the next release of software or cross project software. Real world data mining applications, including software defect prediction domain, must address the issue of learning from imbalanced data sets. As pointed out by Khoshgoftaar et al. [1] and Menzies et al. [2], the majority of defects in a software system are located in a...

Relational databases, with vast amounts of data–from financial transactions, marketing surveys, medical records, to health informatics observations– and complex schemas, are ubiquitous in our society. Multirelational classification algorithms have been proposed to learn from such relational repositories, where multiple interconnected tables (relations) are involved. These methods search for rel...

Many real world data mining applications involve learning from imbalanced data sets. Learning from data sets that contain very few instances of the minority (or interesting) class usually produces biased classifiers that have a higher predictive accuracy over the majority class(es), but poorer predictive accuracy over the minority class. SMOTE (Synthetic Minority Over-sampling TEchnique) is spe...

This contribution proposes a Genetic Algorithm for jointly performing a feature selection and granularity learning for Fuzzy RuleBased Classification Systems in the scenario of data-sets with a high imbalance degree. We refer to imbalanced data-sets when the class distribution is not uniform, a situation that it is present in many real application areas. The aim of this work is to get more comp...