Add to ORKGWe introduce an approach to learning from imbalanced class distributions that does not change the underlying data distribution. The ICC algorithm decomposes majority classes into smaller sub-classes that create a more balanced class distribution. In this paper, we explain how ICC can not only addressthe class imbalance problem but may also increase the expressive power of the hypothesis space. We validate ICC and analyze alternative decomposition methods on well-known machine learning datasets as well as new problems in pervasive computing. Our results indicate that ICC performs as well or better than existing approaches to handling class imbalance
Michigan-style learning classifier systems (LCSs) are online machine learning techniques that increm...
Class-imbalance is a common problem in machine learning practice. Typical Imbalanced Learning (IL) m...
Class imbalance, as a phenomenon of asymmetry, has an adverse effect on the performance of most mach...
Most existing classification approaches assume the underlying training set is evenly distributed. In...
Abstract. Many real world datasets exhibit skewed class distributions in which almost all instances ...
Abstract In machine learning problems, dierences in prior class probabilities|or class imbalances|ha...
Abstract—In the last decade, class imbalance has attracted a huge amount of attention from researche...
In many applications of data mining, class imbalance is noticed when examples in one class are overr...
Assigning class labels to instances is a key component of the machine learning technique known as cl...
Assigning class labels to instances is a key component of the machine learning technique known as cl...
Abstract—Class-imbalance is very common in real data min-ing tasks. Previous studies focused on bina...
There are several aspects that might influence the performance achieved by existing learning systems...
Since many important real-world classification problems involve learning from unbalanced data, the c...
The class imbalance is a critical problem in classification tasks related to many real world applica...
Abstract—Multiple classifier systems tend to suffer from out-voting when new concept classes need to...
Michigan-style learning classifier systems (LCSs) are online machine learning techniques that increm...
Class-imbalance is a common problem in machine learning practice. Typical Imbalanced Learning (IL) m...
Class imbalance, as a phenomenon of asymmetry, has an adverse effect on the performance of most mach...
Most existing classification approaches assume the underlying training set is evenly distributed. In...
Abstract. Many real world datasets exhibit skewed class distributions in which almost all instances ...
Abstract In machine learning problems, dierences in prior class probabilities|or class imbalances|ha...
Abstract—In the last decade, class imbalance has attracted a huge amount of attention from researche...
In many applications of data mining, class imbalance is noticed when examples in one class are overr...
Assigning class labels to instances is a key component of the machine learning technique known as cl...
Assigning class labels to instances is a key component of the machine learning technique known as cl...
Abstract—Class-imbalance is very common in real data min-ing tasks. Previous studies focused on bina...
There are several aspects that might influence the performance achieved by existing learning systems...
Since many important real-world classification problems involve learning from unbalanced data, the c...
The class imbalance is a critical problem in classification tasks related to many real world applica...
Abstract—Multiple classifier systems tend to suffer from out-voting when new concept classes need to...
Michigan-style learning classifier systems (LCSs) are online machine learning techniques that increm...
Class-imbalance is a common problem in machine learning practice. Typical Imbalanced Learning (IL) m...
Class imbalance, as a phenomenon of asymmetry, has an adverse effect on the performance of most mach...