Tag: outlier detection

Researchers at the University of Calabria have developed RLAX, a reinforcement learning framework that identifies compact, interpretable feature subspaces explaining ...

Researchers in Seoul have rebuilt the classic extreme studentized deviate test into an online, self-tuning algorithm that detects anomalies in ...

South African statisticians have developed a double-standardization procedure using robust statistics that simultaneously detects spikes, level shifts, and irregular diurnal ...

Italian researchers have developed a standardised workflow that cleans historical intensity data, simulates ground shaking from candidate faults, and statistically ...

Researchers at Yanshan University have developed ADPOD, an outlier detection algorithm that improves density-peak clustering with an affinity-based density measure, ...

A factorial ANOVA study across five Iranian basins shows that the most important machine learning modeling decision for precipitation prediction ...

Researchers at Xiamen University of Technology have developed an entropy-driven clustering method that automatically tunes its own thresholds to separate ...

A large-scale study from the University of São Paulo tested three strategies for detecting outliers in categorical and mixed data ...

A leakage-free benchmark of ten chronic-disease cohorts finds that no outlier-removal method beats doing nothing, while data-cleaning steps silently delete ...

Researchers have developed 3CPO, a Poisson-based clustering algorithm that groups count data accurately while automatically identifying which columns of a ...