Results 221 to 230 of about 154,181 (262)

On the Class Imbalance Problem

2008 Fourth International Conference on Natural Computation, 2008
The class imbalance problem has been recognized in many practical domains and a hot topic of machine learning in recent years. In such a problem, almost all the examples are labeled as one class, while far fewer examples are labeled as the other class, usually the more important class.
Xinjian Guo, Yilong Yin, Cailing Dong 0003, Gongping Yang 0001, Guang-Tong Zhou   +4 more
openaire   +1 more source

The class imbalance problem in deep learning

Machine Learning, 2022
Deep learning has recently unleashed the ability for Machine learning (ML) to make unparalleled strides. It did so by confronting and successfully addressing, at least to a certain extent, the knowledge bottleneck that paralyzed ML and artificial intelligence for decades.
Kushankur Ghosh, Colin Bellinger, Roberto Corizzo, Paula Branco, Bartosz Krawczyk, Nathalie Japkowicz   +5 more
openaire   +1 more source

Class Imbalance, Redux

2011 IEEE 11th International Conference on Data Mining, 2011
Class imbalance (i.e., scenarios in which classes are unequally represented in the training data) occurs in many real-world learning tasks. Yet despite its practical importance, there is no established theory of class imbalance, and existing methods for handling it are therefore not well motivated.
Byron C. Wallace, Kevin Small, Carla E. Brodley, Thomas A. Trikalinos   +3 more
openaire   +1 more source

The CURE for Class Imbalance

2019
Addressing the class imbalance problem is critical for several real world applications. The application of pre-processing methods is a popular way of dealing with this problem. These solutions increase the rare class examples and/or decrease the normal class cases.
Colin Bellinger, Paula Branco, Luís Torgo   +2 more
openaire   +2 more sources

Exploratory Under-Sampling for Class-Imbalance Learning

Sixth International Conference on Data Mining (ICDM'06), 2006
Undersampling is a popular method in dealing with class-imbalance problems, which uses only a subset of the majority class and thus is very efficient. The main deficiency is that many majority class examples are ignored. We propose two algorithms to overcome this deficiency. EasyEnsemble samples several subsets from the majority class, trains a learner
Xu-Ying Liu, Jianxin Wu 0001, Zhi-Hua Zhou   +2 more
openaire   +2 more sources

Ordinal Class Imbalance with Ranking

2017
Classification datasets, which feature a skewed class distribution, are said to be class imbalance. Traditional methods favor the larger classes. We propose pairwise ranking as a method for imbalance classification so that learning compares pairs of observations from each class, and therefore both contribute equally to the decision boundary.
Ricardo P. M. Cruz, Kelwin Fernandes, Joaquim F. Pinto da Costa, María Pérez-Ortiz 0001, Jaime S. Cardoso 0001   +4 more
openaire   +1 more source

Fighting Class Imbalance with Contrastive Learning

2021
Medical image datasets are hard to collect, expensive to label, and often highly imbalanced. The last issue is underestimated, as typical average metrics hardly reveal that the often very important minority classes have a very low accuracy. In this paper, we address this problem by a feature embedding that balances the classes using contrastive ...
Yassine Marrakchi, Osama Makansi, Thomas Brox   +2 more
openaire   +1 more source

The Class Imbalance Problem

2016
We focus on a special category of pattern recognition problems that arise in cases when the set of training patterns is significantly biased towards a particular class of patterns. This is the so-called Class Imbalance Problem which hinders the performance of many standard classifiers.
Dionisios N. Sotiropoulos, George A. Tsihrintzis   +1 more
openaire   +1 more source

Class imbalance and the curse of minority hubs

Knowledge-Based Systems, 2013
Most machine learning tasks involve learning from high-dimensional data, which is often quite difficult to handle. Hubness is an aspect of the curse of dimensionality that was shown to be highly detrimental to k-nearest neighbor methods in high-dimensional feature spaces.
Nenad Tomasev, Dunja Mladenic
openaire   +1 more source

Class imbalances versus small disjuncts

ACM SIGKDD Explorations Newsletter, 2004
It is often assumed that class imbalances are responsible for significant losses of performance in standard classifiers. The purpose of this paper is to the question whether class imbalances are truly responsible for this degradation or whether it can be explained in some other way.
Taeho Jo 0001, Nathalie Japkowicz
openaire   +1 more source