Results 251 to 260 of about 2,374,745 (297)

An Improved Measurement of the Imbalanced Dataset

2018
Imbalanced classification is a classification problem that violates the assumption of uniform distribution of samples. In such problems, traditional imbalanced datasets are measured in terms of the imbalance of sample size, without considering the distribution information, which has a more important impact on the classification performance, so the ...
Chunkai Zhang, Ying Zhou, Yingyang Chen, Changqing Qi, Xuan Wang 0002, Lifeng Dong   +5 more
openaire   +1 more source

An investigation of bankruptcy prediction in imbalanced datasets

Decision Support Systems, 2018
Abstract Previous studies of bankruptcy prediction in imbalanced datasets analyze either the loss of prediction due to data imbalance issues or treatment methods for dealing with this issue. The current article presents a combined investigation of the degree of imbalance, loss of performance, and treatment methods.
David Veganzones, Eric Séverin
openaire   +1 more source

Discrimination aware classification for imbalanced datasets

Proceedings of the 22nd ACM international conference on Information & Knowledge Management, 2013
The problem of learning a discrimination aware model has recently received attention in the data mining community. Various methods and improved models have been proposed, with the main approach being the detection of a discrimination sensitive attribute.
Goce Ristanoski, Wei Liu 0007, James Bailey 0001   +2 more
openaire   +1 more source

Improving GBDT Performance on Imbalanced Datasets: An Empirical Study of Class-Balanced Loss Functions

Neurocomputing
Class imbalance remains a significant challenge in machine learning, particularly for tabular data classification tasks. While Gradient Boosting Decision Trees (GBDT) models have proven highly effective for such tasks, their performance can be ...
Jiaqi Luo, Yuan Yuan, Shixin Xu
semanticscholar   +1 more source

Comparing SVM ensembles for imbalanced datasets

2010 10th International Conference on Intelligent Systems Design and Applications, 2010
Real life datasets often suffer from the problem of class imbalance, which thwarts supervised learning process. In such data sets examples of positive (minority) class are significantly less than those of negative (majority) class leading to severe class imbalance.
Vasudha Bhatnagar, Manju Bhardwaj, Ashish Mahabal   +2 more
openaire   +1 more source

DEBOHID: A differential evolution based oversampling approach for highly imbalanced datasets

Expert systems with applications, 2021
Class distribution of the samples in the dataset is one of the critical factors affecting the classification success. Classifiers trained with imbalanced datasets classify majority class samples more successfully than minority class samples. Oversampling,
Ersin Kaya, Sedat Korkmaz, M. A. Şahman, A. Cinar   +3 more
semanticscholar   +1 more source

BalancerGNN: Balancer Graph Neural Networks for imbalanced datasets: A case study on fraud detection

Neural Networks
Fraud detection for imbalanced datasets is challenging due to machine learning models inclination to learn the majority class. Imbalance in fraud detection datasets affects how graphs are built, an important step in many Graph Neural Networks (GNNs).
Mallika Boyapati, Ramazan S. Aygun
semanticscholar   +1 more source

Simulating Complexity Measures on Imbalanced Datasets

2020
Classification tasks using imbalanced datasets are not challenging on their own. Classification models perform poorly on the minority class when the datasets present other difficulties, such as class overlap and complex decision border. Data complexity measures can identify such difficulties, better dealing with imbalanced datasets.
Victor H. Barella, Luís Paulo F. Garcia, André C. P. L. F. de Carvalho   +2 more
openaire   +1 more source

Boosting prediction performance on imbalanced dataset

International Journal of Information and Communication Technology, 2018
Mining from imbalance data is an important problem in algorithmic and performance evaluation. When a dataset is imbalanced, the classification technique is not equal considering both the classes. It is obvious that the standard classifiers are not suitable to deal with imbalanced data, since they will likely classify all the instances into the majority
Masoumeh Zareapoor, Pourya Shamsolmoali
openaire   +1 more source

An Adaptive Oversampling Technique for Imbalanced Datasets

2018
Class imbalance is one of the challenging problems in classification domain of data mining. This is particularly so because of the inability of the classifiers in classifying minority examples correctly when data is imbalanced. Further, the performance of the classifiers gets deteriorated due to the presence of imbalance within class in addition to ...
Shaukat Ali Shahee, Usha Ananthakumar
openaire   +1 more source