A support vector machine classifying RCT abstracts with TF-IDF features reached 91% accuracy and an F1 of 0.84, suggesting a 70% workload reduction on one dataset.
Class imbalance learning methods for support vector machines
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Viability of machine learning to reduce workload in systematic review screenings in the health sciences: a working paper
A support vector machine classifying RCT abstracts with TF-IDF features reached 91% accuracy and an F1 of 0.84, suggesting a 70% workload reduction on one dataset.