Results 231 to 240 of about 154,181 (262)

Active learning for class imbalance problem

Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007
The class imbalance problem has been known to hinder the learning performance of classification algorithms. Various real-world classification tasks such as text categorization suffer from this phenomenon. We demonstrate that active learning is capable of solving the problem.
Seyda Ertekin, Jian Huang 0002, C. Lee Giles   +2 more
openaire   +1 more source

The Class-Imbalance Problem for High-Dimensional Class Prediction

2012 11th International Conference on Machine Learning and Applications, 2012
The goal of class prediction studies is to develop rules to accurately predict the class membership of new subjects. The classifiers differ in the way they combine the values of the variables available for each subject. Frequently the classifiers are developed using class-imbalanced data, where the number of samples in each class is not equal. Standard
Lara Lusa, Rok Blagus
openaire   +1 more source

On the Existence of a Threshold in Class Imbalance Problems

2015 IEEE International Conference on Systems, Man, and Cybernetics, 2015
One common approach to class imbalance problem is the resampling of data. However this strategy has some drawbacks, e.g., Unnecessary noise or the possibility of throwing out useful information. These inconveniences may be avoided or minimized by using a class proportion threshold allowing to identify when the imbalance data represent a problem to the ...
Evandro J. R. Silva, Cleber Zanchettin
openaire   +1 more source

Class Imbalance Oriented Logistic Regression

2014 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, 2014
Class-imbalance is quite common in real world. For the imbalanced class distribution, traditional state-of-the-art classifiers do not work well on imbalanced data sets. In this paper, we apply logistic regression model to class-imbalance problem, and propose a novel algorithm called CILR (Class Imbalance oriented Logistic Regression) to tackle ...
Yadong Dong, Huaping Guo, Weimei Zhi, Ming Fan   +3 more
openaire   +1 more source

Strategies for learning in class imbalance problems

Pattern Recognition, 2003
A set of examples or training set (TS) is said to be imbalanced if one of the classes is represented by a very small number of cases compared to the other classes. Following the common practice [1,2], we consider only two-class problems and therefore, the examples are either positive or negative (that is, either from the minority class or the majority ...
Ricardo Barandela, José Salvador Sánchez 0001, Vicente García 0001, E. Rangel   +3 more
openaire   +1 more source

Binary ranking for ordinal class imbalance

Pattern Analysis and Applications, 2018
Imbalanced classification has been extensively researched in the last years due to its prevalence in real-world datasets, ranging from very different topics such as health care or fraud detection. This literature has long been dominated by variations of the same family of solutions (e.g. mainly resampling and cost-sensitive learning).
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

Alleviating class imbalance problem in data mining

2013 21st Signal Processing and Communications Applications Conference (SIU), 2013
The class imbalance problem in two-class data sets is one of the most important problems. When samples of one class in a training data set vastly outnumber samples of the other class, standard machine learning algorithms tend to be overwhelmed by the majority class and ignore the minority class.
Akkenzhe Sarmanova, Songul Albayrak
openaire   +2 more sources

The class imbalance problem

Nature Methods, 2021
Fadel M. Megahed, Ying-Ju Chen, Aly Megahed, Yuya Ong, Naomi Altman, Martin Krzywinski   +5 more
openaire   +1 more source

The class imbalance problem: a systematic study

Intell. Data Anal., 2002
Summary: In machine learning problems, differences in prior class probabilities -- or class imbalances -- have been reported to hinder the performance of some standard classifiers, such as decision trees. This paper presents a systematic study aimed at answering three different questions.
Nathalie Japkowicz, Shaju Stephen
openaire   +2 more sources

Remedies for Severe Class Imbalance

2013
When modeling discrete classes, the relative frequencies of the classes can have a significant impact on the effectiveness of the model. An imbalance occurs when one or more classes have very low proportions in the training data as compared to the other classes. Imbalance can be present in any data set or application, and hence, the practitioner should
Max Kuhn, Kjell Johnson
openaire   +1 more source