Results 31 to 40 of about 103,585 (306)

A method for increasing accuracy of credit imbalanced data [PDF]

فصلنامه بورس اوراق بهادار
The main goal of this research is to provide a method that can be used to increase the accuracy of credit imbalance data. Financial fraud is a fundamental problem that affects both the financial sector and life and plays an important role in affecting ...
Arash GhorbanniaDelavar, Sadaf Sadat Ziya   +1 more
doaj   +1 more source

Evaluating Misclassifications in Imbalanced Data [PDF]

, 2006
Evaluating classifier performance with ROC curves is popular in the machine learning community. To date, the only method to assess confidence of ROC curves is to construct ROC bands. In the case of severe class imbalance with few instances of the minority class, ROC bands become unreliable.
William Elazmeh, Nathalie Japkowicz, Stan Matwin   +2 more
openaire   +1 more source

Oversampling Techniques for Imbalanced Data in Regression

Expert Systems with Applications, 2023
Our study addresses the challenge of imbalanced regression data in Machine Learning (ML) by introducing tailored methods for different data structures. We adapt K-Nearest Neighbor Oversampling-Regression (KNNOR-Reg), originally for imbalanced classification, to address imbalanced regression in low population datasets, evolving to KNNOR-Deep Regression (
Samir Brahim Belhaouari, Ashhadul Islam, Khelil Kassoul, Ala I. Al-Fuqaha, Abdesselam Bouzerdoum   +4 more
openaire   +2 more sources

Bicriteria Oversampling for Imbalanced Data Classification

Procedia Computer Science, 2022
The paper proposes bicriteria oversampling strategy for mining imbalanced data. We use two specialized criteria for oversampling -classification potential and distance from the borderline between minority and majority instances. The potential is to be maximized and the distance minimized.
Joanna Jedrzejowicz, Piotr Jedrzejowicz
openaire   +1 more source

On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems [PDF]

, 2011
The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data.
Xia Hong, Hong, Xia, Harris, C. J., Ming Gao, Sheng Chen, Gao, Ming, Chris J. Harris, Chen, Sheng, Gao, M., Harris, Chris, Chen, S.   +10 more
core   +1 more source

A critical assessment of imbalanced class distribution problem: the case of predicting freshmen student attrition [PDF]

, 2013
Predicting student attrition is an intriguing yet challenging problem for any academic institution. Class-imbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification
Thammasiri, Dech, Delen, Dursun, Meesad, Phayung, Kasap, Nihat   +3 more
core   +1 more source

Semantic concept detection in imbalanced datasets based on different under-sampling strategies [PDF]

, 2011
Semantic concept detection is a very useful technique for developing powerful retrieval or filtering systems for multimedia data. To date, the methods for concept detection have been converging on generic classification schemes.
Guo, Jinlin, null Jinlin Guo, Cathal Gurrin, Colum Foley, null Songyang Lao, Gurrin, Cathal, Foley, Colum, Lao, Songyang   +7 more
core   +1 more source

Survey on highly imbalanced multi-class data [PDF]

, 2022
Machine learning technology has a massive impact on society because it offers solutions to solve many complicated problems like classification, clustering analysis, and predictions, especially during the COVID-19 pandemic.
Abdul Hamid, Mohd Hakim, Mohamed, Azlinah, Yusoff, Marina   +2 more
core   +1 more source

A New Big Data Model Using Distributed Cluster-Based Resampling for Class-Imbalance Problem

Applied Computer Systems, 2019
The class imbalance problem, one of the common data irregularities, causes the development of under-represented models. To resolve this issue, the present study proposes a new cluster-based MapReduce design, entitled Distributed Cluster-based Resampling ...
Terzi Duygu Sinanc, Sagiroglu Seref
doaj   +1 more source

Spectral clustering with imbalanced data [PDF]

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
Spectral clustering (SC) and graph-based semi-supervised learning (SSL) algorithms are sensitive to how graphs are constructed from data. In particular if the data has proximal and unbalanced clusters these algorithms can lead to poor performance on well-known graphs such as $k$-NN, full-RBF, $ε$-graphs. This is because the objectives such as Ratio-Cut
Jing Qian, Venkatesh Saligrama
openaire   +3 more sources