Moreover, the algorithm presented by Tastan et al. is based on supervised learning and training from known interactions between HIV-1 and human proteins.
Here, we employ classifiers that are based on supervised machine leaning, where a training set is applied for each classifier.
For instance, Anvik and Murphy (2011) proposed an approach based on supervised machine learning that requires training to create a classifier.
Although these studies have shown the advantages of AI-based approaches for induction motor fault diagnosis, most of these approaches are based on supervised learning, in which high quality training data with good coverage of true failure conditions are required to perform model training [15].
For example, popular data-driven approaches to NLP are based on supervised learning, which requires substantial amounts of tailored training data, typically built through manual annotation by annotators who need both linguistic and clinical knowledge.
Also, if credibility of tweets would need to be evaluated, a model may have to be developed based on supervised machine learning, where annotated data would be utilised for training of the model (e.g. [6, 7, 36]).
Instead, we must use a statistical classifier based on supervised machine learning, where the model is fitted and evaluated by iterative training and testing on independent, randomly selected subsets of the data.
Our approach to stance classification of tweets is based on supervised machine learning, where a sample of tweets is first manually annotated and then used to train and evaluate a classifier.
Based on supervised learning and similarity measurements, we propose a Recursive Feature Addition (RFA), recursively employ supervised learning to obtain the highest training accuracy and add a subsequent gene based on the similarity between the chosen features and the candidates to minimize the redundancy within the feature set.
The event extraction system is based on supervised machine learning, i.e. it performs predictions for unknown cases based on a model automatically learned from manually annotated training data, here derived from that provided in the BioNLP'09 Shared Task.
