Abstract: Concept drift presents significant challenge in many real-world data stream regression tasks, particularly in industrial applications. This challenge becomes even more complex when concept ...
Effective record linkage in big data, particularly in imbalanced datasets, is a critical yet highly challenging task due to the inherent complexity involved. This article utilizes an ...
ABSTRACT: Current high-dimensional feature screening methods still face significant challenges in handling mixed linear and nonlinear relationships, controlling redundant information, and improving ...
A team of international researchers has unveiled a powerful new machine learning framework, “FraudX AI,” designed to detect credit card fraud with high precision on real-world, highly imbalanced ...
1 Tecnologico de Monterrey, School of Engineering and Sciences, Monterrey, Mexico 2 Smart Computing Laboratory, Centro de Investigación en Computación, Instituto Politecnico Nacional, Mexico, Mexico ...
Abstract: During the collection of time-series data, many reasons lead to imbalanced and incomplete datasets. Consequently, it becomes challenging to develop deep convolutional models without ...
Showcasing impactful machine learning projects tackling real-world issues with Decision Trees, SVM, Time Series, Random Forest, Logistic Regression, and Gradient Boosting. Demonstrates advanced ...
This notebook is a study of the application of sklearn Logistic Regression model and analysis of metric quality with a focus on the impact of imbalanced data. The problem presented is the analysis of ...
Repeatedly doing the same type of activity — whether it’s running, lifting or sitting — can have serious downsides. By Hilary Achauer When you head out for your daily run, with each stride you’re ...
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