Predicting the Industry of Users on Social Media
Table 6: Performance of feature concatenation (early fusion) and stacking (late fusion) on the development set.
['Meta-classifiers', 'Feature Concatenation  [ITALIC] mAcc', 'Feature Concatenation  [ITALIC] MAcc', 'Stacking  [ITALIC] mAcc', 'Stacking  [ITALIC] MAcc']
After establishing the promise of creating an ensemble of classifiers, we implement two meta-classification approaches. First, we combine our classifiers using features concatenation (or early fusion). Starting with our content-based classifier (Text), we successively add the features derived from each metadata element. Even though all these four feature concatenation ensembles outperform the content-based classifier in the development set, they fail to outperform the Occu classifier. We notice a constant improvement for both metrics when adding more classifiers to our ensemble except for the Gloc classifier, which slightly reduces the performance. The best result is achieved using an ensemble of the Text, Occu, Intro, and Inter L0 classifiers; the respective performance on the test set is an mAcc of 0.643 and an MAcc of 0.564.