In the experiment of imbalanced problems, 50 imbalanced data sets from the Knowledge Extraction based on Evolutionary Learning (KEEL: http://www.keel.es/) are used in this paper. Every data set is a 5x3 cell with 5 rows and 3 columns. Every row corresponds to the data in one fold of the 5-folds cross-validation. The first column is the training data of minority class. The second column is the training data of majority class. The last column is the testing data. For all detailed sample in corresponding element, the last column is the label
Classification is one of the critical task in datamining. Many classifiers exist for classification ...
This thesis focuses on the study of machine learning and pattern recognition algorithms for imbalanc...
The problem of imbalanced data has a heavy impact on the performance of learning models. In the case...
One popular approach for imbalance learning is weighting samples in rare classes with high cost and ...
The file attached to this record is the author's final peer reviewed version. The Publisher's final ...
Abstract This paper investigates the capabilities of evolutionary on-line rule-based systems, also c...
There is an increasing interest in the application of Evolutionary Algorithms (EAs) to induce classi...
Many real-life problems can be described as unbalanced, where the number of instances belonging to o...
Classification of data with imbalanced class distribution has encountered a significant drawback by ...
There is an increasing interest in application of Evolutionary Algo-rithms to induce classification ...
The aim of this paper is to investigate the effects of combining various sampling and ensemble class...
Oversampling method is one of the effective ways to deal with imbalance classification problems. Thi...
Ensembles are often capable of greater prediction accuracy than any of their individual members. As ...
Learning from imbalanced data is an important problem in data mining research. Much research has add...
Data imbalance is one of the most difficult problems in machine learning. The improved ensemble lear...
Classification is one of the critical task in datamining. Many classifiers exist for classification ...
This thesis focuses on the study of machine learning and pattern recognition algorithms for imbalanc...
The problem of imbalanced data has a heavy impact on the performance of learning models. In the case...
One popular approach for imbalance learning is weighting samples in rare classes with high cost and ...
The file attached to this record is the author's final peer reviewed version. The Publisher's final ...
Abstract This paper investigates the capabilities of evolutionary on-line rule-based systems, also c...
There is an increasing interest in the application of Evolutionary Algorithms (EAs) to induce classi...
Many real-life problems can be described as unbalanced, where the number of instances belonging to o...
Classification of data with imbalanced class distribution has encountered a significant drawback by ...
There is an increasing interest in application of Evolutionary Algo-rithms to induce classification ...
The aim of this paper is to investigate the effects of combining various sampling and ensemble class...
Oversampling method is one of the effective ways to deal with imbalance classification problems. Thi...
Ensembles are often capable of greater prediction accuracy than any of their individual members. As ...
Learning from imbalanced data is an important problem in data mining research. Much research has add...
Data imbalance is one of the most difficult problems in machine learning. The improved ensemble lear...
Classification is one of the critical task in datamining. Many classifiers exist for classification ...
This thesis focuses on the study of machine learning and pattern recognition algorithms for imbalanc...
The problem of imbalanced data has a heavy impact on the performance of learning models. In the case...