Results 61 to 70 of about 4,590,312 (339)

Teknik Weighting untuk Mengatasi Ketidakseimbangan Kelas Pada Prediksi Churn Menggunakan XGBoost, LightGBM, dan CatBoost

Techno.Com, 2023
Churn merupakan kondisi dimana seseorang berpindah dari satu layanan ke layanan yang lain. Churn pelanggan menjadi masalah yang meningkat cukup signifikan dan menjadi tantangan utama yang harus dihadapi banyak perusahaan perbankan karena memiki peran ...
Wahyu Nugraha, Muhamad Syarif
doaj   +1 more source

Federated Learning and Class Imbalances

CoRR
Federated Learning (FL) enables collaborative model training across decentralized devices while preserving data privacy. However, real-world FL deployments face critical challenges such as data imbalances, including label noise and non-IID distributions.
Siqi Zhu, Joshua D. Kaggie
openaire   +2 more sources

Class-Imbalance Privacy-Preserving Federated Learning for Decentralized Fault Diagnosis With Biometric Authentication

IEEE Transactions on Industrial Informatics, 2022
Privacy protection as a major concern of the industrial big data enabling entities makes the massive safety-critical operation data of a wind turbine unable to exert its great value because of the threat of privacy leakage.
Shixiang Lu, Zhiwei Gao, Qifa Xu, Cuixia Jiang, A. Zhang, Xiangxiang Wang   +5 more
semanticscholar   +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

Spatiotemporal and quantitative analyses of phosphoinositides – fluorescent probe—and mass spectrometry‐based approaches

FEBS Letters, EarlyView.
Fluorescent probes allow dynamic visualization of phosphoinositides in living cells (left), whereas mass spectrometry provides high‐sensitivity, isomer‐resolved quantitation (right). Their synergistic use captures complementary aspects of lipid signaling. This review illustrates how these approaches reveal the spatiotemporal regulation and quantitative
Hiroaki Kajiho, Shin Morioka, Junko Sasaki, Takehiko Sasaki   +3 more
wiley   +1 more source

Rethinking Class Imbalance in Machine Learning

CoRR, 2023
Imbalance learning is a subfield of machine learning that focuses on learning tasks in the presence of class imbalance. Nearly all existing studies refer to class imbalance as a proportion imbalance, where the proportion of training samples in each class is not balanced.
openaire   +2 more sources

Selecting and Interpreting Multiclass Loss and Accuracy Assessment Metrics for Classifications with Class Imbalance: Guidance and Best Practices

Remote Sensing
Evaluating classification accuracy is a key component of the training and validation stages of thematic map production, and the choice of metric has profound implications for both the success of the training process and the reliability of the final ...
Sarah Farhadpour, Timothy A. Warner, Aaron E. Maxwell   +2 more
semanticscholar   +1 more source

Structural insights into an engineered feruloyl esterase with improved MHET degrading properties

FEBS Letters, EarlyView.
A feruloyl esterase was engineered to mimic key features of MHETase, enhancing the degradation of PET oligomers. Structural and computational analysis reveal how a point mutation stabilizes the active site and reshapes the binding cleft, expading substrate scope.
Panagiota Karampa, Konstantinos Makryniotis, Theofani‐Iosifina Sousani, Evangelos Topakas, Vangelis Daskalakis, Maria Dimarogona   +5 more
wiley   +1 more source

Lattice and Imbalance Informed Multi-Label Learning

IEEE Access, 2020
In a multi-label dataset, an instance is given a single representation across all possible labels. Despite the mutual sharing of instances among the labels, the membership of the instances vary from label to label.
Payel Sadhukhan, Sarbani Palit
doaj   +1 more source

Enhancing classification performance of multi-class imbalanced data using the OAA-DB algorithm

, 2012
In data classification, the problem of imbalanced class distribution has attracted many attentions. Most efforts have used to investigate the problem mainly for binary classification.
Wong, K.W., Piyasak Jeatrakul, Kok Wai Wong, Jeatrakul, P.   +3 more
core   +1 more source