A Thorough Exploitation of Distance-Based Meta-Features for Automated Text Classification

The definition of a set of informative features capable of representing and discriminating documents is paramount for the task of automatically classifying documents. In this doctoral dissertation, we present the most comprehensive study so far on the role of meta-features (high-level features built from lower-level ones) as an alternative for representing documents. We start by proposing new sets of (meta-)features that exploit distance measures in the original (bag-of-words) feature space to summarize potentially complex relationships between documents. We then (i) analyze the discriminative power of such meta-features with novel multi-objective feature selection strategies; (ii) provide new GPU implementations to reduce computational time; (iii) enrich distance relationships with labeled or context-specific information; (iv) adapt the proposed meta-features for tasks as hard as sentiment analysis. Our experimental results show that our meta-features can achieve remarkable classification results by distance exploitation, being the state-of-the-art in many situations and scenarios.