HDLTex: Hierarchical Deep Learning for Text Classification
TABLE III: HDLTex and Baseline Accuracy of three WOS datasets
['[EMPTY]', 'WOS-11967 Methods', 'WOS-11967 Methods', 'WOS-11967 Accuracy', 'WOS-46985 Methods', 'WOS-46985 Methods', 'WOS-46985 Accuracy', 'WOS-5736 Methods', 'WOS-5736 Methods', 'WOS-5736 Accuracy']
The baseline tests compare three conventional document classification approaches (naïve Bayes and two versions of SVM) and stacking SVM with three deep learning approaches (DNN, RNN, and CNN). In this set of tests the RNN outperforms the others for all three WOS data sets. CNN performs second-best for three data sets. The third data set is the smallest of the three and has the fewest labels so the differences among the three best performers are not large. These results show that overall performance improvement for general document classification is obtainable with deep learning approaches compared to traditional methods. Overall, naïve Bayes does much worse than the other methods throughout these tests. As for the tests of classifying these documents within a hierarchy, the HDLTex approaches with stacked, deep learning architectures clearly provide superior performance. For data set WOS−11967, the best accuracy is obtained by the combination RNN for the first level of classification and DNN for the second level. This gives accuracies of 94% for the first level, 92% for the second level and 86% overall. This is significantly better than all of the others except for the combination of CNN and DNN. For data set WOS−46985 the best scores are again achieved by RNN for level one but this time with RNN for level 2. The closest scores to this are obtained by CNN and RNN in levels 1 and 2, respectively. Finally the simpler data set WOS−5736 has a winner in CNN at level 1 and CNN at level 2, but there is little difference between these scores and those obtained by two other HDLTex architectures: DNN with CNN and RNN with CNN.