SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary
The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …
considered" de facto" standard in the framework of learning from imbalanced data. This is …
An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes
Classification problems involving multiple classes can be addressed in different ways. One
of the most popular techniques consists in dividing the original data set into two-class …
of the most popular techniques consists in dividing the original data set into two-class …
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
Training classifiers with datasets which suffer of imbalanced class distributions is an
important problem in data mining. This issue occurs when the number of examples …
important problem in data mining. This issue occurs when the number of examples …
An investigation of bankruptcy prediction in imbalanced datasets
D Veganzones, E Séverin - Decision Support Systems, 2018 - Elsevier
Previous studies of bankruptcy prediction in imbalanced datasets analyze either the loss of
prediction due to data imbalance issues or treatment methods for dealing with this issue …
prediction due to data imbalance issues or treatment methods for dealing with this issue …
Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data
Classification with big data has become one of the latest trends when talking about learning
from the available information. The data growth in the last years has rocketed the interest in …
from the available information. The data growth in the last years has rocketed the interest in …
A comparative study of improved GA and PSO in solving multiple traveling salesmen problem
Multiple traveling salesman problem (MTSP) is a generalization of the classic traveling
salesman problem (TSP). Compared to TSP, MTSP is more common in real-life applications …
salesman problem (TSP). Compared to TSP, MTSP is more common in real-life applications …
A Scalo gram-based CNN ensemble method with density-aware smote oversampling for improving bearing fault diagnosis
Machine learning (ML) based bearing fault detection is an emerging application of Artificial
Intelligence (AI) that has proven its utility in effectively classifying various faults for timely …
Intelligence (AI) that has proven its utility in effectively classifying various faults for timely …
Investigating airline passenger satisfaction: Data mining method
T Noviantoro, JP Huang - Research in Transportation Business & …, 2022 - Elsevier
In the current competitive environment, winning excellent services in the aviation industry
can gain competitive advantages. Aviation companies should understand how their services …
can gain competitive advantages. Aviation companies should understand how their services …
Detecting privacy requirements from User Stories with NLP transfer learning models
Context: To provide privacy-aware software systems, it is crucial to consider privacy from the
very beginning of the development. However, developers do not have the expertise and the …
very beginning of the development. However, developers do not have the expertise and the …
An oversampling method of unbalanced data for mechanical fault diagnosis based on MeanRadius-SMOTE
F Duan, S Zhang, Y Yan, Z Cai - Sensors, 2022 - mdpi.com
With the development of machine learning, data-driven mechanical fault diagnosis methods
have been widely used in the field of PHM. Due to the limitation of the amount of fault data, it …
have been widely used in the field of PHM. Due to the limitation of the amount of fault data, it …