Prediction of pipe failures in water supply networks using logistic regression and support vector classification
Companies in charge of water supply networks are making a huge effort to optimally plan
the annual replacements of pipes. This would save costs, enable a higher quality of service …
the annual replacements of pipes. This would save costs, enable a higher quality of service …
[HTML][HTML] Asset management analytics for urban water mains: a literature review
This study presents a review of the state-of-the-art literature on water pipe failure predictions,
assessment of water losses risk, optimal pipe maintenance plans, and maintenance …
assessment of water losses risk, optimal pipe maintenance plans, and maintenance …
A modified generative adversarial network for fault diagnosis in high-speed train components with imbalanced and heterogeneous monitoring data
Data-driven methods are widely considered for fault diagnosis in complex systems.
However, in practice the between-class imbalance due to limited faulty samples may …
However, in practice the between-class imbalance due to limited faulty samples may …
Improved dynamic kernel principal component analysis for fault detection
The dynamic kernel principal component analysis (DKPCA) has attracted significant
attention with regards to the monitoring of nonlinear and dynamic industrial processes …
attention with regards to the monitoring of nonlinear and dynamic industrial processes …
Credit card fraud detection by modelling behaviour pattern using hybrid ensemble model
The fraud detection system in banking organisation relies on data-driven approach to
identify the fraudulent transactions. In real time, detection of each and every fraudulent …
identify the fraudulent transactions. In real time, detection of each and every fraudulent …
Automated imbalanced classification via meta-learning
N Moniz, V Cerqueira - Expert Systems with Applications, 2021 - Elsevier
Imbalanced learning is one of the most relevant problems in machine learning. However, it
faces two crucial challenges. First, the amount of methods proposed to deal with such …
faces two crucial challenges. First, the amount of methods proposed to deal with such …
Feature selection and ensemble learning techniques in one-class classifiers: an empirical study of two-class imbalanced datasets
CF Tsai, WC Lin - IEEE Access, 2021 - ieeexplore.ieee.org
Class imbalance learning is an important research problem in data mining and machine
learning. Most solutions including data levels, algorithm levels, and cost sensitive …
learning. Most solutions including data levels, algorithm levels, and cost sensitive …
A new machine learning-based method for android malware detection on imbalanced dataset
DT Dehkordy, A Rasoolzadegan - Multimedia Tools and Applications, 2021 - Springer
Nowadays, malware applications are dangerous threats to Android devices, users,
developers, and application stores. Researchers are trying to discover new methods for …
developers, and application stores. Researchers are trying to discover new methods for …
No Free Lunch in imbalanced learning
N Moniz, H Monteiro - Knowledge-Based Systems, 2021 - Elsevier
Abstract The No Free Lunch (NFL) theorems have sparked intense debate since their
publication, from theoretical and practical perspectives. However, to this date, no discussion …
publication, from theoretical and practical perspectives. However, to this date, no discussion …
Classifier selection and ensemble model for multi-class imbalance learning in education grants prediction
Y Sun, Z Li, X Li, J Zhang - Applied Artificial Intelligence, 2021 - Taylor & Francis
Ensemble learning combines base classifiers to improve the performance of the models and
obtains a higher classification accuracy than a single classifier. We propose a multi …
obtains a higher classification accuracy than a single classifier. We propose a multi …