Add to ORKGAbstract. Imbalance data constitutes a great difficulty for most algo-rithms learning classifiers. However, as recent works claim, class imbalance is not a problem in itself and performance degradation is also associated with other factors related to the distribution of the data as the presence of noisy and borderline examples in the areas surrounding class boundaries. This contribution proposes to extend SMOTE with a noise filter called Iterative-Partitioning Filter (IPF), which can overcome these problems. The properties of this proposal are discussed in a controlled experimen-tal study against SMOTE and its most well-known generalizations. The results show that the new proposal performs better than exiting SMOTE generalizations for all t...
We introduce a method to deal with the problem of learning from imbalanced data sets, where examples...
Abstract — Many real-world applications have problems when learning from imbalanced data sets, such ...
The performance of the data classification has encountered a problem when the data distribution is i...
Imbalance data constitutes a great difficulty for most algorithms learning classifiers. However, as...
International audienceDealing with imbalanced datasets at the preprocessing level is an efficient st...
Abstract. Many real world datasets exhibit skewed class distributions in which almost all instances ...
Imbalanced data sets in real-world applications have a majority class with normal instances and a mi...
In our research, we review some of the modern used oversampling techniques for tackling Class Imbala...
Douzas, G., Bação, F., & Last, F. (2018). Improving imbalanced learning through a heuristic oversamp...
Many traditional approaches to pattern classifi- cation assume that the problem classes share simila...
Classification of data with imbalanced class distribution has encountered a significant drawback by ...
Binary datasets are considered imbalanced when one of their two classes has less than 40% of the tot...
Classification problem for imbalanced datasets is pervasive in a lot of data mining domains. Imbalan...
The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered \de fac...
In the field of machine learning, the problem of class imbalance considerably impairs the performanc...
We introduce a method to deal with the problem of learning from imbalanced data sets, where examples...
Abstract — Many real-world applications have problems when learning from imbalanced data sets, such ...
The performance of the data classification has encountered a problem when the data distribution is i...
Imbalance data constitutes a great difficulty for most algorithms learning classifiers. However, as...
International audienceDealing with imbalanced datasets at the preprocessing level is an efficient st...
Abstract. Many real world datasets exhibit skewed class distributions in which almost all instances ...
Imbalanced data sets in real-world applications have a majority class with normal instances and a mi...
In our research, we review some of the modern used oversampling techniques for tackling Class Imbala...
Douzas, G., Bação, F., & Last, F. (2018). Improving imbalanced learning through a heuristic oversamp...
Many traditional approaches to pattern classifi- cation assume that the problem classes share simila...
Classification of data with imbalanced class distribution has encountered a significant drawback by ...
Binary datasets are considered imbalanced when one of their two classes has less than 40% of the tot...
Classification problem for imbalanced datasets is pervasive in a lot of data mining domains. Imbalan...
The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered \de fac...
In the field of machine learning, the problem of class imbalance considerably impairs the performanc...
We introduce a method to deal with the problem of learning from imbalanced data sets, where examples...
Abstract — Many real-world applications have problems when learning from imbalanced data sets, such ...
The performance of the data classification has encountered a problem when the data distribution is i...