Abstract
Deep learning is often used in text classification tasks for its efficiency and proficiency in modelling nonlinear processes. However, using this type of machine learning takes more time and processing power than using shallow learning algorithms. The scripts in this pubication makes it possible to investigate if a combination of shallow and deep learning techniques can be used in increasing the classification performance for automated systematic review software. To find these situations, simulations were run on a prepared dataset using different classifiers, switching from shallow to deep networks. This GitHub repository hosts information and code for research on model switching during simulations and active classification. It is accompanied by the asreview-plugin-model-switcher plugin, for software called ASReview.
Original language | English |
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Publisher | Zenodo |
Media of output | Online |
DOIs | |
Publication status | Published - 2021 |
Keywords
- machine learning active learning ASReview text-classification