Results 31 to 40 of about 40,989 (265)

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

Binary classification for imbalanced datasets using a novel metric method

Egyptian Informatics Journal
This work proposes a kernel amplification method with non-stationary characteristics for binary classification of non-noisy imbalanced datasets. Our methodology features two key innovations, including that a derived non-stationary kernel construction ...
Jian Zheng, Shengye Wang, Huyong Yan, Haichao Sun   +3 more
doaj   +1 more source

Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers [PDF]

International Journal of Computational Intelligence Systems, 2012
Determining whether an imprecise dataset is imbalanced is not immediate. The vagueness in the data causes that the prior probabilities of the classes are not precisely known, and therefore the degree of imbalance can also be uncertain.
AnaM. Palacios, Luciano Sánchez, Inés Couso   +2 more
doaj   +1 more source

Active Class Incremental Learning for Imbalanced Datasets [PDF]

, 2020
Accepted in IPCV workshop from ...
Eden Belouadah, Adrian Popescu 0001, Umang Aggarwal, Léo Saci   +3 more
openaire   +2 more sources

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

Posterior Re-calibration for Imbalanced Datasets

CoRR, 2020
Accepted to NeurIPS ...
Junjiao Tian, Yen-Cheng Liu, Nathaniel Glaser, Yen-Chang Hsu, Zsolt Kira   +4 more
openaire   +3 more sources

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

A Hybrid Approach Handling Imbalanced Datasets [PDF]

, 2009
Several binary classification problems exhibit imbalance in class distribution, influencing system learning. Indeed, traditional machine learning algorithms are biased towards the majority class, thus producing poor predictive accuracy over the minority one. To overcome this limitation, many approaches have been proposed up to now to build artificially
openaire   +2 more sources

Diversity and complexity in neural organoids

FEBS Letters, EarlyView.
Neural organoid research aims to expand genetic diversity on one side and increase tissue complexity on the other. Chimeroids integrate multiple donor genomes within single organoids. Self‐organising multi‐identity organoids, exogenous cell seeding, or enforced assembly of region‐specific organoids contribute to tissue complexity.
Ilaria Chiaradia, Madeline A. Lancaster
wiley   +1 more source