Add to ORKGA dataset is said to be imbalanced when its classes are disproportionately represented in terms of the number of instances they contain. This problem is common in applications such as medical diagnosis of rare diseases, detection of fraudulent calls, signature recognition. In this paper we propose an alternative method for imbalanced learning, which balances the dataset using an undersampling strategy. We show that ClusterOSS outperforms OSS, which is the method ClusterOSS is based on. Moreover, we show that the results can be further improved by combining ClusterOSS with random oversampling
A Dataset is unbalanced when the class of interest (minority class) is much smaller or rarer than no...
In many applications of data mining, class imbalance is noticed when examples in one class are overr...
There are several aspects that might influence the performance achieved by existing learning systems...
A dataset is said to be imbalanced when its classes are disproportionately represented in terms of t...
Class imbalance is an important problem, encountered in machine learning applications, where one cla...
Nowadays, most real-world datasets suffer from the problem of imbalanced distribution of data sample...
Douzas, G., Bação, F., & Last, F. (2018). Improving imbalanced learning through a heuristic oversamp...
Abstract—Under-sampling is a popular method in deal-ing with class-imbalance problems, which uses on...
The improvements in technology and computation have promoted a global adoption of Data Science. It i...
In this thesis several sampling methods for Statistical Learning with imbalanced data have been impl...
Imbalanced class problem (machine learning) is a problem that arises because of the significant diff...
Learning from imbalanced data poses significant challenges for the classifier. This becomes even mor...
There is an unprecedented amount of data available. This has caused knowledge discovery to garner at...
Undersampling has been widely used in the class-imbalance learning area. The main deficiency of most...
Abstract—The class imbalance problem is a well-known classi-fication challenge in machine learning t...
A Dataset is unbalanced when the class of interest (minority class) is much smaller or rarer than no...
In many applications of data mining, class imbalance is noticed when examples in one class are overr...
There are several aspects that might influence the performance achieved by existing learning systems...
A dataset is said to be imbalanced when its classes are disproportionately represented in terms of t...
Class imbalance is an important problem, encountered in machine learning applications, where one cla...
Nowadays, most real-world datasets suffer from the problem of imbalanced distribution of data sample...
Douzas, G., Bação, F., & Last, F. (2018). Improving imbalanced learning through a heuristic oversamp...
Abstract—Under-sampling is a popular method in deal-ing with class-imbalance problems, which uses on...
The improvements in technology and computation have promoted a global adoption of Data Science. It i...
In this thesis several sampling methods for Statistical Learning with imbalanced data have been impl...
Imbalanced class problem (machine learning) is a problem that arises because of the significant diff...
Learning from imbalanced data poses significant challenges for the classifier. This becomes even mor...
There is an unprecedented amount of data available. This has caused knowledge discovery to garner at...
Undersampling has been widely used in the class-imbalance learning area. The main deficiency of most...
Abstract—The class imbalance problem is a well-known classi-fication challenge in machine learning t...
A Dataset is unbalanced when the class of interest (minority class) is much smaller or rarer than no...
In many applications of data mining, class imbalance is noticed when examples in one class are overr...
There are several aspects that might influence the performance achieved by existing learning systems...