Results 11 to 20 of about 527,011 (237)

Stop Oversampling for Class Imbalance Learning: A Review

IEEE Access, 2022
For the last two decades, oversampling has been employed to overcome the challenge of learning from imbalanced datasets. Many approaches to solving this challenge have been offered in the literature. Oversampling, on the other hand, is a concern. That is,
Ahmad S. Tarawneh, Ahmad B. Hassanat, Ghada Awad Altarawneh, Abdullah Almuhaimeed   +3 more
doaj   +1 more source

An AUC-based Permutation Variable Importance Measure for Random Forests [PDF]

, 2012
The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs).
A Estabrooks, AL Boulesteix, AL Boulesteix, Anne-Laure Boulesteix, C Chen, C Liu, C Strobl, Carolin Strobl, F Briggs, G Batista, J Chang, J Van Hulse, J Van Hulse, K Nicodemus, KK Nicodemus, KK Nicodemus, KK Nicodemus, L Breiman, M Calle, M Cummings, M Khalilia, M Kubat, M Pepe, N Japkowicz, R Blagus, Silke Janitza, T Fawcett, T Hothorn, T Hothorn, T Khoshgoftaar, WJ Lin, Y Huang, Y Sun, Y Xie   +33 more
core   +3 more sources

Addressing class imbalance in soil movement predictions [PDF]

Natural Hazards and Earth System Sciences
Landslides threaten human life and infrastructure, resulting in fatalities and economic losses. Monitoring stations provide valuable data for predicting soil movement, which is crucial in mitigating this threat.
P. Kumar, P. Priyanka, K. V. Uday, V. Dutt   +3 more
doaj   +1 more source

Class Balanced Loss for Image Classification

IEEE Access, 2020
In the study of image classification, neural network learning relies heavily on datasets. Due to variability in the difficulty of collecting images in reality, datasets tend to have class imbalance problems, which undoubtedly increases the difficulty of ...
Lin Wang, Chaoli Wang, Zhanquan Sun, Shuqun Cheng, Lei Guo   +4 more
doaj   +1 more source

Cost-Sensitive Pattern-Based classification for Class Imbalance problems

IEEE Access, 2019
In several problems, contrast pattern-based classifiers produce high accuracy and provide an explanation of the result in terms of the patterns used for classification. However, class imbalance problems are a great challenge for these classifiers because
Octavio Loyola-Gonzalez, Jose F.C.O. Martinez-Trinidad, Jesus Ariel Carrasco-Ochoa, Milton Garcia-Borroto   +3 more
doaj   +1 more source

A Hybrid Algorithm-Level Ensemble Model for Imbalanced Credit Default Prediction in the Energy Industry

Energies, 2022
Credit default prediction for the energy industry is essential to promoting the healthy development of the energy industry in China. While previous studies have constructed various credit default prediction models with brilliant performance, the class ...
Kui Wang, Jie Wan, Gang Li, Hao Sun
doaj   +1 more source

A dynamic linear model for heteroscedastic LDA under class imbalance [PDF]

, 2019
Linear Discriminant Analysis (LDA) yields the optimal Bayes classifier for binary classification for normally distributed classes with equal covariance.
Brusey, James, Gaura, Elena, Gyamfi, Sarfo, Hunt, Andrew   +3 more
core   +1 more source

Weighted Point Cloud Augmentation for Neural Network Training Data Class-Imbalance [PDF]

, 2019
Recent developments in the field of deep learning for 3D data have demonstrated promising potential for end-to-end learning directly from point clouds.
Boehm, Jan, Griffiths, David
core   +2 more sources

Efficient treatment of outliers and class imbalance for diabetes prediction [PDF]

, 2020
Learning from outliers and imbalanced data remains one of the major difficulties for machine learning classifiers. Among the numerous techniques dedicated to tackle this problem, data preprocessing solutions are known to be efficient and easy to ...
KORKONTZELOS, YANNIS, NNAMOKO, NONSO
core   +1 more source

Survey on deep learning with class imbalance

Journal of Big Data, 2019
The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in many real ...
Justin M. Johnson, Taghi M. Khoshgoftaar
doaj   +1 more source