Shamsudin, Haziqah (2025) Integration Of Dynamic Loss Function Autoencoder In Boost. PhD thesis, Perpustakaan Hamzah Sendut.
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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.
| Item Type: | Thesis (PhD) |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science |
| Divisions: | Pusat Pengajian Sains Matematik (School of Mathematical Sciences) > Thesis |
| Depositing User: | Mr Hasmizar Mansor |
| Date Deposited: | 11 May 2026 02:08 |
| Last Modified: | 11 May 2026 02:13 |
| URI: | http://eprints.usm.my/id/eprint/64139 |
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