Feature selection in machine learning prediction systems for renewable energy applications
This paper focuses on feature selection problems that arise in renewable energy
applications. Feature selection is an important problem in machine learning, both in …
applications. Feature selection is an important problem in machine learning, both in …
Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review
Atmospheric extreme events cause severe damage to human societies and ecosystems.
The frequency and intensity of extremes and other associated events are continuously …
The frequency and intensity of extremes and other associated events are continuously …
A network surveillance approach using machine learning based control charts
Network surveillance, ie, the detection of anomalous behaviour in communications in a
network, has become an important issue in recent years. In this field, techniques of statistical …
network, has become an important issue in recent years. In this field, techniques of statistical …
An ensemble oversampling model for class imbalance problem in software defect prediction
Software systems are now ubiquitous and are used every day for automation purposes in
personal and enterprise applications; they are also essential to many safety-critical and …
personal and enterprise applications; they are also essential to many safety-critical and …
Manifold regularized stacked autoencoders-based feature learning for fault detection in industrial processes
J Yu, C Zhang - Journal of Process Control, 2020 - Elsevier
Multivariate statistical process control (MSPC) has been widely employed for process fault
detection. Recently, deep neural networks (DNNs), ie, stacked autoencoder (SAE) enjoys its …
detection. Recently, deep neural networks (DNNs), ie, stacked autoencoder (SAE) enjoys its …
[HTML][HTML] A new feature selection method based on a validity index of feature subset
C Liu, W Wang, Q Zhao, X Shen, M Konan - Pattern Recognition Letters, 2017 - Elsevier
The wrapper feature selection method can achieve high classification accuracy. However,
the cross-validation scheme of the wrapper method in evaluation phase is very expensive …
the cross-validation scheme of the wrapper method in evaluation phase is very expensive …
A framework for software defect prediction and metric selection
Automated software defect prediction is an important and fundamental activity in the domain
of software development. However, modern software systems are inherently large and …
of software development. However, modern software systems are inherently large and …
Towards design and feasibility analysis of DePaaS: AI based global unified software defect prediction framework
Featured Application DePaaS has the potential to be used as a global, shared platform for
availing software defects prediction services by choosing appropriate base project, defect …
availing software defects prediction services by choosing appropriate base project, defect …
Fault detection and recognition of multivariate process based on feature learning of one-dimensional convolutional neural network and stacked denoised autoencoder
Multivariate process pattern recognition (MPPR) is essential towards continuous quality
control task. A challenging problem is to extract effective features from complex process …
control task. A challenging problem is to extract effective features from complex process …
Analysis, characterization, prediction and attribution of extreme atmospheric events with machine learning: a review
Atmospheric Extreme Events (EEs) cause severe damages to human societies and
ecosystems. The frequency and intensity of EEs and other associated events are increasing …
ecosystems. The frequency and intensity of EEs and other associated events are increasing …