Ensuring equitable impact of machine learning models across different societal groups is of
utmost importance for real-world machine learning applications. Prior research in fairness …

With advancements in artificial intelligence (AI), explainable AI (XAI) has emerged as a
promising tool for enhancing the explainability of complex machine learning models …

Categorical encoders transform categorical features into numerical representations that are
indispensable for a wide range of machine learning models. Existing encoder benchmark …

Federated Learning (FL) provides a privacy-preserving and decentralized approach to
collaborative machine learning for multiple FL clients. The contribution estimation …

Neural networks are important computational models used in the domains of artificial
intelligence and software engineering. Parameters of a neural network are obtained via …

Mobile Edge Computing (MEC) has great potential to facilitate cheap and fast customer
behavior analysis (CBA). Model splitting, widely adopted in collaborative learning of MEC …

Noise in data significantly influences decision-making in the data science process. In fact, it
has been shown that noise in data generation processes leads practitioners to find simpler …

Integrating machine learning and data mining is crucial for processing big data and
extracting valuable insights to enhance decision-making. However, imbalanced target …

Tabular data, a prevalent data type across various domains, presents unique challenges
due to its heterogeneous nature and complex structural relationships. Achieving high …

Federated Learning (FL) provides a privacy-preserving mechanism for distributed training of
machine learning models on networked devices (eg, mobile devices, IoT edge nodes). It …