ENSEMBLE LEARNING WITH HIGHLY VARIABLE CLASS-BASED PERFORMANCE

Ensemble Learning with Highly Variable Class-Based Performance

This paper proposes a novel model-agnostic method for weighting the outputs of base classifiers in machine learning (ML) ensembles.Our approach uses class-based weight coefficients assigned to every output class in each learner in the ensemble.This is particularly useful when the base classifiers have highly Bruschetta variable performance across c

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Evaluation of the best fit distribution for partial duration series of daily rainfall in Madinah, western Saudi Arabia

Rainfall frequency analysis is an essential tool for the design of water related infrastructure.It can be used to Brogue Lace Up Boots predict future flood magnitudes for a given magnitude and frequency of extreme rainfall events.This study analyses the application of rainfall partial duration series (PDS) in the vast growing urban Madinah city loc

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