Feature selection and feature learning in machine learning applications for gas turbines: A review

J Xie, M Sage, YF Zhao - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
The progress of machine learning (ML) in the past years has opened up new opportunities
to the field of gas turbine (GT) modelling. However, successful implementation of ML …

High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning

SM Erfani, S Rajasegarar, S Karunasekera, C Leckie - Pattern Recognition, 2016 - Elsevier
High-dimensional problem domains pose significant challenges for anomaly detection. The
presence of irrelevant features can conceal the presence of anomalies. This problem, known …

A survey on semi-supervised feature selection methods

R Sheikhpour, MA Sarram, S Gharaghani… - Pattern recognition, 2017 - Elsevier
Feature selection is a significant task in data mining and machine learning applications
which eliminates irrelevant and redundant features and improves learning performance. In …

A pragmatic investigation of energy consumption and utilization models in the urban sector using predictive intelligence approaches

SK Mohapatra, S Mishra, HK Tripathy, AK Bhoi… - Energies, 2021 - mdpi.com
Energy consumption is a crucial domain in energy system management. Recently, it was
observed that there has been a rapid rise in the consumption of energy throughout the …

Development of machine learning methods to predict the compressive strength of fiber-reinforced self-compacting concrete and sensitivity analysis

MH Nguyen, HB Ly - Construction and Building Materials, 2023 - Elsevier
Fiber-reinforced self-compacting concrete (FRSCC), a great combination of self-compacting
concrete (SCC) and fiber, plays a vital role as a potential construction material. Improving …

On the platform but will they buy? Predicting customers' purchase behavior using deep learning

N Chaudhuri, G Gupta, V Vamsi, I Bose - Decision Support Systems, 2021 - Elsevier
A thorough understanding of online customer's purchase behavior will directly boost e-
commerce business performance. Existing studies have overtly focused on purchase …

Robust large margin deep neural networks

J Sokolić, R Giryes, G Sapiro… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
The generalization error of deep neural networks via their classification margin is studied in
this paper. Our approach is based on the Jacobian matrix of a deep neural network and can …

Unsupervised feature selection via nonnegative spectral analysis and redundancy control

Z Li, J Tang - IEEE Transactions on Image Processing, 2015 - ieeexplore.ieee.org
In many image processing and pattern recognition problems, visual contents of images are
currently described by high-dimensional features, which are often redundant and noisy …

EEG-based mental fatigue measurement using multi-class support vector machines with confidence estimate

KQ Shen, XP Li, CJ Ong, SY Shao… - Clinical …, 2008 - Elsevier
OBJECTIVE: Automatic measurement and the monitoring of mental fatigue are invaluable for
preventing mental-fatigue related accidents. We test an EEG-based mental-fatigue …

A robust least squares support vector machine for regression and classification with noise

X Yang, L Tan, L He - Neurocomputing, 2014 - Elsevier
Least squares support vector machines (LS-SVMs) are sensitive to outliers or noise in the
training dataset. Weighted least squares support vector machines (WLS-SVMs) can partly …