Learning from class-imbalanced data: Review of methods and applications
Rare events, especially those that could potentially negatively impact society, often require
humans' decision-making responses. Detecting rare events can be viewed as a prediction …
humans' decision-making responses. Detecting rare events can be viewed as a prediction …
A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches
Classifier learning with data-sets that suffer from imbalanced class distributions is a
challenging problem in data mining community. This issue occurs when the number of …
challenging problem in data mining community. This issue occurs when the number of …
Credit card fraud detection under extreme imbalanced data: a comparative study of data-level algorithms
Credit card fraud is one of the biggest cybercrimes faced by users. Intelligent machine
learning based fraudulent transaction detection systems are very effective in real-world …
learning based fraudulent transaction detection systems are very effective in real-world …
[HTML][HTML] Optimization of skewed data using sampling-based preprocessing approach
In the past few years, classification has undergone some major evolution. With a constant
surge of the amount of data gathered from different sources, efficient processing and …
surge of the amount of data gathered from different sources, efficient processing and …
Daily air quality index forecasting with hybrid models: A case in China
S Zhu, X Lian, H Liu, J Hu, Y Wang, J Che - Environmental pollution, 2017 - Elsevier
Air quality is closely related to quality of life. Air pollution forecasting plays a vital role in air
pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air …
pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air …
Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine
W Mao, L He, Y Yan, J Wang - Mechanical Systems and Signal Processing, 2017 - Elsevier
Diagnosis of bearings generally plays an important role in fault diagnosis of mechanical
system, and machine learning has been a promising tool in this field. In many real …
system, and machine learning has been a promising tool in this field. In many real …
Addressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling
In the classification framework there are problems in which the number of examples per
class is not equitably distributed, formerly known as imbalanced data sets. This situation is a …
class is not equitably distributed, formerly known as imbalanced data sets. This situation is a …
Ozone concentration forecast method based on genetic algorithm optimized back propagation neural networks and support vector machine data classification
Y Feng, W Zhang, D Sun, L Zhang - Atmospheric Environment, 2011 - Elsevier
Multi Artificial Neural Network (ANN) models are used to forecast ozone concentration on
single-site for a better forecast accuracy in huge dataset condition. Support Vector Machine …
single-site for a better forecast accuracy in huge dataset condition. Support Vector Machine …
Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model
Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring
and forecasting of air quality parameters have become popular as an important topic in …
and forecasting of air quality parameters have become popular as an important topic in …
[PDF][PDF] Deep air: forecasting air pollution in Beijing, China
V Reddy, P Yedavalli, S Mohanty… - Environmental …, 2018 - ischool.berkeley.edu
Air pollution in urban environments has risen steadily in the last several decades. Such
cities as Beijing and Delhi have experienced rises to dangerous levels for citizens. As a …
cities as Beijing and Delhi have experienced rises to dangerous levels for citizens. As a …