Results 11 to 20 of about 2,374,745 (297)

Impact of imbalanced features on large datasets

Frontiers in Big Data
The exponential growth of image and video data motivates the need for practical real-time content-based searching algorithms. Features play a vital role in identifying objects within images.
Waleed Albattah, Rehan Ullah Khan
doaj   +4 more sources

Imbalanced SVM‐Based Anomaly Detection Algorithm for Imbalanced Training Datasets [PDF]

ETRI Journal, 2017
Abnormal samples are usually difficult to obtain in production systems, resulting in imbalanced training sample sets. Namely, the number of positive samples is far less than the number of negative samples.
GuiPing Wang, JianXi Yang, Ren Li
doaj   +2 more sources

Predicting Default Risk on Peer-to-Peer Lending Imbalanced Datasets

IEEE Access, 2021
In the past few years, Peer-to-Peer lending (P2P lending) has grown rapidly in the world. The main idea of P2P lending is disintermediation and removing the intermediaries like banks.
Yen-Ru Chen, Jenq-Shiou Leu, Sheng-An Huang, Jui-Tang Wang, Jun-Ichi Takada   +4 more
doaj   +2 more sources

The Effects of Imbalanced Datasets on Machine Learning Algorithms in Predicting Student Performance

JOIV: International Journal on Informatics Visualization
Predictive analytics technologies are becoming increasingly popular in higher education institutions. Students' grades are one of the most critical performance indicators educators can use to predict their academic achievement.
Khaled Mahmud Sujon, Rohayanti Hassan, Alif Ridzuan Khairudin, Sim Hiew Moi, Muhammad Luqman Mohd Shafie, Zainuri Saringat, Aldo Erianda   +6 more
doaj   +2 more sources

Pneumonia Detection from Chest X-Ray Images Using Deep Learning and Transfer Learning for Imbalanced Datasets. [PDF]

J Imaging Inform Med
Alshanketi F, Alharbi A, Kuruvilla M, Mahzoon V, Siddiqui ST, Rana N, Tahir A.   +6 more
europepmc   +2 more sources

LoRAS: an oversampling approach for imbalanced datasets [PDF]

Machine Learning, 2020
AbstractThe Synthetic Minority Oversampling TEchnique (SMOTE) is widely-used for the analysis of imbalanced datasets. It is known that SMOTE frequently over-generalizes the minority class, leading to misclassifications for the majority class, and effecting the overall balance of the model.
Saptarshi Bej, Narek Davtyan, Markus Wolfien, Mariam Nassar, Olaf Wolkenhauer   +4 more
openaire   +3 more sources

A Multi-Schematic Classifier-Independent Oversampling Approach for Imbalanced Datasets

IEEE Access, 2021
Labelled imbalanced data, used for classification problems, have an unequal distribution of samples over the classes. Traditional classification models, such as random forest, gradient boosting, face a problem when dealing with imbalanced datasets.
Saptarshi Bej, Kristian Schulz, Prashant Srivastava, Markus Wolfien, Olaf Wolkenhauer   +4 more
doaj   +1 more source

Adaptive Age Estimation towards Imbalanced Datasets

Applied Sciences, 2023
Current age estimation datasets often have a skewed long-tail distribution with significant data imbalance, rather than an ideal uniform distribution for each category.
Zhiang Dong, Xiaoqiang Li
doaj   +1 more source

Probability-Based Synthetic Minority Oversampling Technique

IEEE Access, 2023
Many real-life datasets suffer from class imbalance, where one or more classes are under-represented in the dataset, resulting in reduced classifier performance, with the expected decline in quality of procedures depending on the classification results ...
Najwa Altwaijry
doaj   +1 more source

Single-Point Crossover and Jellyfish Optimization for Handling Imbalanced Data Classification Problem

IEEE Access, 2022
The imbalanced datasets and their classification has pulled in as a hot research topic over the years. It is used in different fields, for example, security, finance, health, and many others.
Abeer S. Desuky, Yomna M. Elbarawy, Samina Kausar, Asmaa Hekal Omar, Sadiq Hussain   +4 more
doaj   +1 more source