A TSC framework separates historical attendance sequences from future labels and uses LSTM-FCN with BFL or G-Mean loss to achieve approximately 80% balanced accuracy for proactive absenteeism prediction on simulated data.
Predicting Absenteeism at Work Using Tree-Based Learners
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A time-series classification framework for individual-level absenteeism prediction under severe class imbalance
A TSC framework separates historical attendance sequences from future labels and uses LSTM-FCN with BFL or G-Mean loss to achieve approximately 80% balanced accuracy for proactive absenteeism prediction on simulated data.