Towards Automated Sexual Violence Report Tracking
Table 7: Performance of different classifiers on the detection and characterization tasks
['Classifier', 'Categories', 'SVM P', 'SVM R', 'SVM F-1', 'GDB P', 'GDB R', 'GDB F-1', 'RDF P', 'RDF R', 'RDF F-1', 'MVC P', 'MVC R', 'MVC F-1']
We experimented with multiple supervised learning algorithms. Specifically, we used Support Vector Machine (SVM) with linear kernel, Random Forest (RDF) with 100 estimators, and Gradient Boosting (GDB). In some cases, the SVM and GDB outperformed others in terms of f1-measure. However, we observed a better performance when all of the three classifiers were combined to build a majority voting classifier (MVC). We used a Python package named scikit-learn to build MVC. It predicts the class label based on the argmax of the sums of the predicted probabilities by SVM, RDF, and GDB. We conducted 5-fold cross-validation. In each fold, 80% of the data was used for training and the remaining 20% was used for testing. Then took the average of the performances of the folds. The highest F1-measure for each category is highlighted in the table. MVC achieved the highest F1-measures in 10 of the 17 total categories.