Results 301 to 310 of about 539,097 (330)

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
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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
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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
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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
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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
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The class imbalance problem

Nature Methods, 2021
Fadel M. Megahed, Ying-Ju Chen, Aly Megahed, Yuya Ong, Naomi Altman, Martin Krzywinski   +5 more
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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
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The class imbalance problem: a systematic study

Intell. Data Anal., 2015
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
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Learning From Class Imbalance

2020
Learning a classifier from imbalanced data is one of the most challenging research problems. Data imbalance occurs when the number of instances belonging to one class is much less than the number of instances belonging to the other class. A standard classifier is biased towards the majority class and therefore misclassifies the minority class instances.
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Handling Class Imbalance by Estimating Minority Class Statistics

2023 International Joint Conference on Neural Networks (IJCNN), 2023
Faizanuddin Ansari, Swagatam Das, Pourya Shamsolmoali   +2 more
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