A systematic review on imbalanced data challenges in machine learning: Applications and solutions
In machine learning, the data imbalance imposes challenges to perform data analytics in
almost all areas of real-world research. The raw primary data often suffers from the skewed …
almost all areas of real-world research. The raw primary data often suffers from the skewed …
[HTML][HTML] Applications of explainable artificial intelligence in finance—a systematic review of finance, information systems, and computer science literature
Digitalization and technologization affect numerous domains, promising advantages but
also entailing risks. Hence, when decision-makers in highly-regulated domains like Finance …
also entailing risks. Hence, when decision-makers in highly-regulated domains like Finance …
Learning from class-imbalanced data: Review of methods and applications
Rare events, especially those that could potentially negatively impact society, often require
humans' decision-making responses. Detecting rare events can be viewed as a prediction …
humans' decision-making responses. Detecting rare events can be viewed as a prediction …
Toward human-understandable, explainable AI
H Hagras - Computer, 2018 - ieeexplore.ieee.org
Recent increases in computing power, coupled with rapid growth in the availability and
quantity of data have rekindled our interest in the theory and applications of artificial …
quantity of data have rekindled our interest in the theory and applications of artificial …
A survey on FinTech
As a new term in the financial industry, FinTech has become a popular term that describes
novel technologies adopted by the financial service institutions. This term covers a large …
novel technologies adopted by the financial service institutions. This term covers a large …
[HTML][HTML] Interpretable machine learning for imbalanced credit scoring datasets
The class imbalance problem is common in the credit scoring domain, as the number of
defaulters is usually much less than the number of non-defaulters. To date, research on …
defaulters is usually much less than the number of non-defaulters. To date, research on …
Evolutionary fuzzy systems for explainable artificial intelligence: Why, when, what for, and where to?
Evolutionary fuzzy systems are one of the greatest advances within the area of
computational intelligence. They consist of evolutionary algorithms applied to the design of …
computational intelligence. They consist of evolutionary algorithms applied to the design of …
A historical account of types of fuzzy sets and their relationships
A Historical Account of Types of Fuzzy Sets and Their Relationships Page 1 IEEE
TRANSACTIONS ON FUZZY SYSTEMS, VOL. 24, NO. 1, FEBRUARY 2016 179 A Historical …
TRANSACTIONS ON FUZZY SYSTEMS, VOL. 24, NO. 1, FEBRUARY 2016 179 A Historical …
[HTML][HTML] 不平衡数据分类方法综述
李艳霞， 柴毅， 胡友强， 尹宏鹏 - 控制与决策, 2019 - kzyjc.alljournals.cn
随着信息技术的快速发展, 各领域的数据正以前所未有的速度产生并被广泛收集和存储,
如何实现数据的智能化处理从而利用数据中蕴含的有价值信息已成为理论和应用的研究热点 …
如何实现数据的智能化处理从而利用数据中蕴含的有价值信息已成为理论和应用的研究热点 …
[HTML][HTML] SMOTE-LOF for noise identification in imbalanced data classification
NU Maulidevi, K Surendro - Journal of King Saud University-Computer …, 2022 - Elsevier
Imbalanced data typically refers to a condition in which several data samples in a certain
problem is not equally distributed, thereby leading to the underrepresentation of one or more …
problem is not equally distributed, thereby leading to the underrepresentation of one or more …