Residualized Factor Adaptationfor Community Social Media Prediction Tasks
Table 3: Comparing R2 using different methods of feature selection. Outcomes are heart disease (HD), fair/poor health (FP), life satisfaction (LS), increased price (IP), and foreclosure rate (FC). FS stands for feature selection. Bold cells have the highest R2 for each outcome.
['[EMPTY]', 'No FS', 'Separated FS', 'Combined FS', 'Early FS']
Here we investigate the impact of feature selection on the overall performance of RFA. We consider three different combination of adaptation and feature selection, as well as adaptation without any feature selection: (1) SeparatedFS: apply feature selection separately on language features and adapted language features; (2) CombinedFS: combine language features and adapted language features into one feature set and then apply feature selection; (3) EarlyFS: apply feature selection on language features, then apply adaptation on the selected features; and (4) NoFS: perform adaptation without any feature selection. SeparatedFS performs better than the others in 3 out of 5 cases, as well as leading the average R2 across all 5 outcomes. In addition, it produces the most stable results in comparison to the other methods. We therefore use this method for RFA.