Results 1 to 10 of about 36,115 (166)

Influence of Preprocessing Methods of Automated Milking Systems Data on Prediction of Mastitis with Machine Learning Models

AgriEngineering
Missing data and class imbalance hinder the accurate prediction of rare events such as dairy mastitis. Resampling and imputation are employed to handle these problems.
Olivier Kashongwe, Tina Kabelitz, Christian Ammon, Lukas Minogue, Markus Doherr, Pablo Silva Boloña, Thomas Amon, Barbara Amon   +7 more
doaj   +1 more source

The Crest factor for trigonometric polynomials. Part I: Approximation theoretical estimates

Journal of Numerical Analysis and Approximation Theory, 2001
The Chebyshev norm of a degree n trigonometric polynomial is estimated against a discrete maximum norm based on equidistant sampling points where, typically, oversampling rather than critical sampling is used. The bounds are derived from various methods
K. Jetter, G. Pfander, G. Zimmermann
doaj   +2 more sources

Imbalanced learning: Improving classification of diabetic neuropathy from magnetic resonance imaging.

PLoS ONE, 2020
One of the fundamental challenges when dealing with medical imaging datasets is class imbalance. Class imbalance happens where an instance in the class of interest is relatively low, when compared to the rest of the data.
Kevin Teh, Paul Armitage, Solomon Tesfaye, Dinesh Selvarajah, Iain D Wilkinson   +4 more
doaj   +1 more source

Decision Support Model for Time Series Data Augmentation Method Selection

IEEE Access
Data augmentation (DA) plays a crucial role in machine learning by improving model generalization and tackling data scarcity issues, particularly prevalent in domains with limited access to sensitive information or rare events.
Dorian Joubaud, Sylvain Kubler, Raoni Lourenco, Maxime Cordy, Yves Le Traon   +4 more
doaj   +1 more source

GMO-AC: Gaussian-Based Minority Oversampling With Adaptive Outlier Filtering and Class Overlap Weighting

IEEE Access
Imbalanced data significantly affects the performance of standard classification models. Data-level approaches primarily use oversampling methods, such as the synthetic minority oversampling technique (SMOTE), to address this problem.
Seung Jee Yang, Kyungjoon Cha
doaj   +1 more source

IMPROVING PERFORMANCE FOR IMBALANCED DATA CLASSIFICATION USING OVERSAMPLING AND CHARACTERISTICS OF EACH CLUSTER

Tạp chí Khoa học
This paper proposes a method to enhance the effectiveness of classifying imbalanced data. The main contribution of the method is integrating the K-means clustering algorithm and the minority oversampling technique VCIR to generate synthetic samples that ...
Phan Anh Phong, Le Van Thanh
doaj   +1 more source

Impact of Adaptive Synthetic on Naïve Bayes Accuracy in Imbalanced Anemia Detection Datasets

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This research aims to analyze the impact of the Adaptive Synthetic (ADASYN) oversampling technique on the performance of the Naïve Bayes classification algorithm on datasets with class imbalance.
Muhammad Khahfi Zuhanda, Lisya Permata, Hartono, Erianto Ongko, Desniarti   +4 more
doaj   +1 more source

GLoW SMOTE-D: Oversampling Technique to Improve Prediction Model Performance of Students Failure in Courses

IEEE Access
The percentage of passing courses is dependent on the assistance provided to students. To ensure the effectiveness of these efforts, identifying students at risk of course failure as early as possible is crucial.
Susana Limanto, Joko Lianto Buliali, Ahmad Saikhu   +2 more
doaj   +1 more source

Enhanced Diagnosis of Thyroid Diseases Through Advanced Machine Learning Methodologies

Sci
Thyroid disease is a health concern related to the thyroid gland, which is vital for controlling the metabolism of the human body. Predominantly affecting women in their fourth or fifth decades of life, thyroid disease can result in physical and mental ...
Osasere Oture, Muhammad Zahid Iqbal, Xining (Ning) Wang   +2 more
doaj   +1 more source

Comparative Analysis of Oversampling and SMOTEENN Techniques in Machine Learning Algorithms for Breast Cancer Prediction

Sistemasi: Jurnal Sistem Informasi
Breast cancer is the leading cause of cancer-related death among women, with one of the major challenges in developing predictive models being the class imbalance in medical datasets.
Tri Yulian, Erliyan Redy Susanto
doaj   +1 more source