# Active Learning for Biomedical Article Classification with Bag of Words and _FastText_ Embeddings
###### Abstract  
In several applications of text classification, training document labels are provided by human evaluators, and therefore, gathering sufficient data for model creation is time consuming and costly. The labeling time and effort may be reduced by active learning, in which classification models are created based on relatively small training sets, which are obtained by collecting class labels provided in response to labeling requests or queries. This is an iterative process with a sequence of models being fitted, and each of them is used to select query articles to be added to the training set for the next one. Such a learning scenario may pose different challenges for machine learning algorithms and text representation methods used for text classification than ordinary passive learning, since they have to deal with very small, often imbalanced data, and the computational expense of both model creation and prediction has to remain low. This work examines how classification algorithms and text representation methods that have been found particularly useful by prior work handle these challenges. The random forest and support vector machines algorithms are coupled with the bag of words and _FastText_ word embedding representations and applied to datasets consisting of scientific article abstracts from systematic literature review studies in the biomedical domain. Several strategies are used to select articles for active learning queries, including uncertainty sampling, diversity sampling, and strategies favoring the minority class. Confidence-based and stability-based early stopping criteria are used to generate active learning termination signals. The results confirm that active learning is a useful approach to creating text classification models with limited access to labeled data, making it possible to save at least half of the human effort needed to assign relevant or irrelevant class labels to training articles. Two of the four examined combinations of classification algorithms and text representation methods were the most successful: the SVM algorithm with the _FastText_ representation and the random forest algorithm with the bag of words representation. Uncertainty sampling turned out to be the most useful query selection strategy, and confidence-based stopping was found more universal and easier to configure than stability-based stopping. active learning; text classification; text representation; systematic literature reviews +
Footnote †: journal: Medical  
deep neural network architectures. The former require transforming text data to a vector representation, and the latter create their own internal representations. The time and effort required to perform the human screening of training data may be reduced by active learning [9; 10]. This is an iterative process in which a sequence of models is created with each of them used to select additional training instances for the next one from the available unlabeled dataset. The general principle is to select instances believed to be the most likely to permit obtaining a better model in the next iteration. The learning system issues a class query for the selected articles, i.e., requests human-assigned class labels for them. Active learning makes it possible to create classification models of satisfactory quality using considerably less training data (and therefore requiring considerably less human effort) than in standard passive learning, but it requires appropriate criteria for query selection and stopping the process.