3 papers
cs.LG2026
Benchmarking Instance-Dependent Label Noise with Controlled Corruptions
Shadman Islam, Agustinus Kristiadi, Mostafa Milani
Synthetic instance-dependent label noise (IDN) benchmarks are widely used to evaluate noisy-label learning methods, yet existing approaches typically generate noise through imperfe…
cs.DB2026
The Human Factor in Data Cleaning: Exploring Preferences and Biases
Hazim AbdElazim, Shadman Islam, Mostafa Milani
Data cleaning is often framed as a technical preprocessing step, yet in practice it relies heavily on human judgment. We report results from a controlled survey study in which part…
cs.DB2026
Heterogeneity in Entity Matching: A Survey and Experimental Analysis
Mohammad Hossein Moslemi, Amir Mousavi, Behshid Behkamal +1
Entity matching (EM) is a fundamental task in data integration and analytics, essential for identifying records that refer to the same real-world entity across diverse sources. In…