Results 41 to 50 of about 98,421 (281)

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   +1 more source

CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification

, 2017
Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature.
Ahmed, Sajid, Farid, Dewan Md., Jani, Md. Rafsan, Mahbub, Asif, Rayhan, Farshid, Shatabda, Swakkhar   +5 more
core   +1 more source

A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets

Abstract and Applied Analysis, 2013
In imbalanced learning methods, resampling methods modify an imbalanced dataset to form a balanced dataset. Balanced data sets perform better than imbalanced datasets for many base classifiers.
Yong Zhang, Dapeng Wang
doaj   +1 more source

Imbalanced Data Classification Method Based on LSSASMOTE

IEEE Access, 2023
Imbalanced data exist extensively in the real world, and the classification of imbalanced data is a hot topic in machine learning. In order to classify imbalanced data more effectively, an oversampling method named LSSASMOTE is proposed in this paper ...
Zhi Wang, Qicheng Liu
doaj   +1 more source

Handling Imbalanced Datasets for Robust Deep Neural Network-Based Fault Detection in Manufacturing Systems

Applied Sciences, 2021
Over the recent years, Industry 4.0 (I4.0) technologies such as the Industrial Internet of Things (IIoT), Artificial Intelligence (AI), and the presence of Industrial Big Data (IBD) have helped achieve intelligent Fault Detection (FD) in manufacturing ...
Jefkine Kafunah, Muhammad Intizar Ali, John G. Breslin   +2 more
doaj   +1 more source

Aggressive prostate cancer is associated with pericyte dysfunction

Molecular Oncology, EarlyView.
Tumor‐produced TGF‐β drives pericyte dysfunction in prostate cancer. This dysfunction is characterized by downregulation of some canonical pericyte markers (i.e., DES, CSPG4, and ACTA2) while maintaining the expression of others (i.e., PDGFRB, NOTCH3, and RGS5).
Anabel Martinez‐Romero, Ane Martinez‐Larrinaga, Joaquim Grego‐Bessa, Saioa Garcia‐Longarte, Hielke van Splunder, Ianire Astobiza, Amaia Ercilla, Laura Bozal‐Basterra, Isabel Mendizabal, Pilar Villacampa, Arkaitz Carracedo, Mariona Graupera   +11 more
wiley   +1 more source

The Distance-Based Balancing Ensemble Method for Data With a High Imbalance Ratio

IEEE Access, 2019
Many classification tasks suffer from the class imbalance problem that seriously hinders the precision of classifiers. The existing algorithms frequently incorrectly categorize new instances into the majority class.
Dong Chen, Xiao-Jun Wang, Changjun Zhou, Bin Wang   +3 more
doaj   +1 more source

Oversampling for Imbalanced Learning Based on K-Means and SMOTE

, 2017
Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced class distributions. While different strategies exist to tackle this problem,
Bacao, Fernando, Douzas, Georgios, Last, Felix   +2 more
core   +1 more source

Crucial parameters for precise copy number variation detection in formalin‐fixed paraffin‐embedded solid cancer samples

Molecular Oncology, EarlyView.
This study shows that copy number variations (CNVs) can be reliably detected in formalin‐fixed paraffin‐embedded (FFPE) solid cancer samples using ultra‐low‐pass whole‐genome sequencing, provided that key (pre)‐analytical parameters are optimized.
Hanne Goris, Vasiliki Siozopoulou, Léon C van Kempen, Anne Sieben, Ella Roelant, Stig Hellemans, Elyne Backx, Laure Sorber, Koen De Winne, Senada Koljenović, Karen Zwaenepoel   +10 more
wiley   +1 more source

A Novel Synthetic Minority Oversampling Technique for Multiclass Imbalance Problems

IEEE Access
Multi-class imbalanced datasets present significant challenges in many real-world classification tasks, where certain classes are severely underrepresented.
Jiao Wang, Norhashidah Awang
doaj   +1 more source