Results 301 to 310 of about 4,590,312 (339)
Some of the next articles are maybe not open access.
Active learning for class imbalance problem
Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007The 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 +2 more
openaire +1 more source
The Class-Imbalance Problem for High-Dimensional Class Prediction
2012 11th International Conference on Machine Learning and Applications, 2012The 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, 2015One 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, 2014Class-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 +3 more
openaire +1 more source
Strategies for learning in class imbalance problems
Pattern Recognition, 2003A 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 +3 more
openaire +1 more source
Binary ranking for ordinal class imbalance
Pattern Analysis and Applications, 2018Imbalanced 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 +4 more
openaire +1 more source
Alleviating class imbalance problem in data mining
2013 21st Signal Processing and Communications Applications Conference (SIU), 2013The 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
Information Sciences, 2019
Imbalanced classification problems are often encountered in many applications. The challenge is that there is a minority class that has typically very little data and is often the focus of attention.
Dina Elreedy, A. Atiya
semanticscholar +1 more source
Imbalanced classification problems are often encountered in many applications. The challenge is that there is a minority class that has typically very little data and is often the focus of attention.
Dina Elreedy, A. Atiya
semanticscholar +1 more source
A broad review on class imbalance learning techniques
Applied Soft Computing, 2023S. Rezvani, Xizhao Wang
semanticscholar +1 more source