DATA AUGMENTATION USING GENERATIVE NETWORKS TO IDENTIFY DEMENTIA
Table 3: Precision (Pr), recall (Rc) and F-scores (Fs) of the Logistic Regression classifier trained using different sets of features extracted from the 60 conversations with 5-fold cross validation. NZ: Non-zero features. F5: Fold 5, the fold close to the average.)
['[BOLD] Feature set', '[BOLD] Feat. No.', '[BOLD] Pr %', '[BOLD] Rc %', '[BOLD] Fs %']
Using the LR classifier and the five fold cross validation approach the precision, recall and F-score of the classifier were calculated first on the original 78 features and then on the 324 features (original+extended features). The columns with majority of zero values were omitted from the feature sets (we call them non-zeros (NZ)). Based on the five fold cross-validation, in each fold out of the total 60 samples, 40 were used in train set, 8 for evaluation and 12 for test. all features selected by RFE. It can be seen, that the NZ features from the original set can achieve around 40% F-score (3.5% increase), which then can be improved further by RFE up to 59%. However, using all features together resulted in a better performance than the original features (F-score of 45.6% compared to the 36.6% F-score). Applying RFE (68 top features) this was further improved to an F-score of 64% (F-score of fold 5 was 58.3%, the closest F-scores to the average). On the last row of the table, the results for the average fold (number 5) is shown. We will refer to this fold as (hallam (F5)). This fold will be used in the following experiments as a fixed train/test partition.