Consumer segmentation is vital for companies to customize their offerings effectively. Our
study explores the application of Large Language Models (LLMs) in marketing research for …

Fraud datasets often times lack consistent and accurate labels, and are characterized by
having high class imbalance where the number of fraudulent examples are far fewer than …

Objective: Identifying fraud in healthcare programs is crucial, as an estimated 3%–10% of
the total healthcare expenditures are lost to fraudulent activities. This study presents a …

Advances in data mining and machine learning continue to transform the healthcare industry
and provide value to medical professionals and patients. In this study, we address the …

LightGBM is a popular Gradient Boosted Decision Tree implementation for classification and
regression tasks. Our contribution is to answer a research question regarding LightGBM. We …

Abstract Machine learning applications for healthcare are resha** the industry with new
tools and services designed to improve the quality of patient care. A challenge common to …

Fraudulent transaction data tend to have several categorical features with high cardinality. It
makes data preprocessing complicated if categories in such features do not have an order …

This study evaluates the classification performance of five encoding techniques for high-
cardinality categorical features. Encoding techniques are tested using popular bagging and …

Many studies have shown that non-default decision thresholds are required to maximize
classification performance on highly imbalanced data sets. Thresholding strategies include …

We present findings from experiments in Medicare fraud detection, that are the result of
research on two new, publicly available datasets. In this research, we employ popular, open …