Improving text classification with vectors of reduced precisionThis research was supported in part by Faculty of Management and Social Communication of the Jagiellonian University and PLGrid Infrastructure.
TABLE V: Times of training and testing for the classifiers on the corpora without or with SVD with different number of components.
['[BOLD] Classifier', '[BOLD] Variant', '[BOLD] Training (seconds) webkb', '[BOLD] Training (seconds) r8', '[BOLD] Training (seconds) r52', '[BOLD] Training (seconds) ng20', '[BOLD] Training (seconds) cade', '[BOLD] Testing (seconds) webkb', '[BOLD] Testing (seconds) r8', '[BOLD] Testing (seconds) r52', '[BOLD] Testing (seconds) ng20', '[BOLD] Testing (seconds) cade']
SVD is the most time–consuming phase in training in comparison to classification. However, it can reduce time of testing. Time of testing using KNNs is higher than other classifiers, because it is proportional to number of documents. Time of precision reduction is negligible.