SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary
The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …
considered" de facto" standard in the framework of learning from imbalanced data. This is …
Machine learning for streaming data: state of the art, challenges, and opportunities
Incremental learning, online learning, and data stream learning are terms commonly
associated with learning algorithms that update their models given a continuous influx of …
associated with learning algorithms that update their models given a continuous influx of …
Learned lessons in credit card fraud detection from a practitioner perspective
Billions of dollars of loss are caused every year due to fraudulent credit card transactions.
The design of efficient fraud detection algorithms is key for reducing these losses, and more …
The design of efficient fraud detection algorithms is key for reducing these losses, and more …
Incremental learning of concept drift from streaming imbalanced data
Learning in nonstationary environments, also known as learning concept drift, is concerned
with learning from data whose statistical characteristics change over time. Concept drift is …
with learning from data whose statistical characteristics change over time. Concept drift is …
Learning from streaming data with concept drift and imbalance: an overview
The primary focus of machine learning has traditionally been on learning from data assumed
to be sufficient and representative of the underlying fixed, yet unknown, distribution. Such …
to be sufficient and representative of the underlying fixed, yet unknown, distribution. Such …
A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: Shallow and deep learning
The objective of this paper is to present a comprehensive review of the contemporary
techniques for fault detection, diagnosis, and prognosis of rolling element bearings (REBs) …
techniques for fault detection, diagnosis, and prognosis of rolling element bearings (REBs) …
An overview on concept drift learning
AS Iwashita, JP Papa - IEEE access, 2018 - ieeexplore.ieee.org
Concept drift techniques aim at learning patterns from data streams that may change over
time. Although such behavior is not usually expected in controlled environments, real-world …
time. Although such behavior is not usually expected in controlled environments, real-world …
Ensemble of subset online sequential extreme learning machine for class imbalance and concept drift
In this paper, a computationally efficient framework, referred to as ensemble of subset online
sequential extreme learning machine (ESOS-ELM), is proposed for class imbalance …
sequential extreme learning machine (ESOS-ELM), is proposed for class imbalance …
Detecting credit card fraud using selected machine learning algorithms
M Puh, L Brkić - 2019 42nd International Convention on …, 2019 - ieeexplore.ieee.org
Due to the immense growth of e-commerce and increased online based payment
possibilities, credit card fraud has become deeply relevant global issue. Recently, there has …
possibilities, credit card fraud has become deeply relevant global issue. Recently, there has …
Weighted online sequential extreme learning machine for class imbalance learning
Most of the existing sequential learning methods for class imbalance learn data in chunks. In
this paper, we propose a weighted online sequential extreme learning machine (WOS-ELM) …
this paper, we propose a weighted online sequential extreme learning machine (WOS-ELM) …