Publication: Integration Of Dynamic Loss Function Autoencoder In Boost
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Date
2025-04
Authors
Shamsudin, Haziqah
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Abstract
Highly class imbalance together with high data complexity (feature overlap and poor class separability), presents a significant challenge in machine learning. Traditional classifiers often exhibit bias towards the majority class, resulting in poor performance on the minority class, which is frequently the class of interest. Existing methods address imbalance or complexity, but rarely both effectively, and often lack adaptivity during training. This thesis addresses these challenges through a series of algorithmic enhancements.
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Machine Learning