[HTML][HTML] A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation
Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …
belonging to one class is lower than the other. Ensemble learning combines multiple models …
[HTML][HTML] From concept drift to model degradation: An overview on performance-aware drift detectors
The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …
machine learning (ML) models. Changes in the system on which the ML model has been …
Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment
COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing
life around the world to a frightening halt and claiming thousands of lives. Due to COVID …
life around the world to a frightening halt and claiming thousands of lives. Due to COVID …
Deep learning for IoT big data and streaming analytics: A survey
In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect
and/or generate various sensory data over time for a wide range of fields and applications …
and/or generate various sensory data over time for a wide range of fields and applications …
[PDF][PDF] Study of variants of extreme learning machine (ELM) brands and its performance measure on classification algorithm
Recently, the feed-forward neural network is functioning with slow computation time and
increased gain. The weight vector and biases in the neural network can be tuned based on …
increased gain. The weight vector and biases in the neural network can be tuned based on …
Online learning: A comprehensive survey
Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …
to tackle some predictive (or any type of decision-making) task by learning from a sequence …
State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives
Accurate state of health (SOH) prediction is significant to guarantee operation safety and
avoid latent failures of lithium-ion batteries. With the development of communication and …
avoid latent failures of lithium-ion batteries. With the development of communication and …
Extreme learning machine for multilayer perceptron
Extreme learning machine (ELM) is an emerging learning algorithm for the generalized
single hidden layer feedforward neural networks, of which the hidden node parameters are …
single hidden layer feedforward neural networks, of which the hidden node parameters are …
Non-iterative and fast deep learning: Multilayer extreme learning machines
In the past decade, deep learning techniques have powered many aspects of our daily life,
and drawn ever-increasing research interests. However, conventional deep learning …
and drawn ever-increasing research interests. However, conventional deep learning …
Trends in extreme learning machines: A review
Extreme learning machine (ELM) has gained increasing interest from various research fields
recently. In this review, we aim to report the current state of the theoretical research and …
recently. In this review, we aim to report the current state of the theoretical research and …