Results 231 to 240 of about 98,421 (281)

CelLink: integrating single-cell multi-omics data with weak feature linkage and imbalanced cell populations. [PDF]

Nucleic Acids Res
Luo X, Huang Y, Zeng H, Tao Y, Bao X, Feng F, Hopkirk AL, Pham T, Bate TSR, Saunders DC, Orchard P, Robertson C, Shrestha S, Spraggins JM, Cartailler JP, Parker SCJ, Brissova M, Liu J.   +17 more
europepmc   +1 more source

Enhanced Prediction of Atrial Fibrillation in Patients With Ischemic Stroke Through Electronic Medical Records and Text Mining: Algorithm Development and Validation.

JMIR Med Inform
Chen YW, Sung SF, Hu YH, Yang YH.
europepmc   +1 more source

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Epileptic Seizure Prediction for Imbalanced Datasets

2019 Medical Technologies Congress (TIPTEKNO), 2019
In this study, the methods used in the classification of imbalanced data sets were applied to EEG signals obtained from epilepsy patients and epileptic seizures were estimated. Firstly, the data set was balanced by using under-sampling, oversampling, and synthetic minority over-sampling technique and classified with Support Vector Machines.
Coşgun, Ercan, Çelebi, A., Güllü, M. K.   +2 more
openaire   +3 more sources

Imbalanced Learning in Massive Phishing Datasets

2020 IEEE 6th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), 2020
Phishing is one of the major threats facing internet users in today’s work. Such attacks continue costing billions of dollars to companies around the words thus requiring more efficient detection techniques to curb the danger. This paper proposes a big data friendly implementation of Multiclass Imbalance Learning in Ensembles through Selective Sampling
Ali Azari, Josephine M. Namayanja, Navneet Kaur, Vasundhara Misal, Suraksha Shukla   +4 more
openaire   +1 more source

Applying Resampling Methods for Imbalanced Datasets to Not So Imbalanced Datasets

2013
Many efforts have been done recently proposing new intelligent resampling methods as a way to solve class imbalance problems; one of the main challenges of the machine learning community nowadays. Usually the purpose of these methods is to balance the classes. However, there are works in the literature showing that those methods can also be suitable to
Olatz Arbelaitz, Ibai Gurrutxaga, Javier Muguerza, Jesús María Pérez   +3 more
openaire   +1 more source

Facial Emotion Recognition in Imbalanced Datasets

Artificial Intelligence and Applications, 2022
The wide usage of computer vision has become popular in the recent years. One of the areas of computer vision that has been studied is facial emotion recognition, which plays a crucial role in the interpersonal communication. This paper tackles the problem of intraclass variances in the face images of emotion recognition datasets. We test the system on
Sarvenaz Ghafourian, Ramin Sharifi, Amirali Baniasadi   +2 more
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Supervised Microalgae Classification in Imbalanced Dataset

2016 5th Brazilian Conference on Intelligent Systems (BRACIS), 2016
Microalgae are unicellular organisms that have physical characteristics such as size, shape or even the present structures. Classifying them manually may require great effort from experts since thousands of microalgae can be found in a small sample of water. Furthermore, the manual classification is not a trivial operation.
Iago Correa, Paulo Drews, Marcio Silva De Souza, Virginia Maria Tavano   +3 more
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To improve classification of imbalanced datasets

2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017
The task of accurately predicting the target class for each case in the data is called classification of data in data mining. Classification of balanced data set is fairly simple and easy to perform but it becomes difficult when the data is not balanced.
Pratyusha Shukla, Kiran Bhowmick
openaire   +1 more source