Haytham Elghazel

TL;DR: The experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning.

TL;DR: Empirical results are provided indicating that RCE, boosted with a recursive feature elimination scheme (RFE) can lead to significant improvement in terms of clustering accuracy, over several state-of-the-art supervised and unsupervised algorithms, with a very limited subset of features.

TL;DR: A novel feature ranking method, termed as Ensemble of Regressor Chains-guided Feature Ranking (ERCFR) to forecast multiple air pollutants simultaneously over two cities, based on a combination of one of the most powerful ensemble methods for Multi-Target Regression problems and the Random Forest permutation importance measure is proposed.

TL;DR: The combination of both rotation-based ensemble construction and Latent Semantic Indexing projection is shown to bring about significant improvements in terms of Average Precision, Coverage, Ranking loss and One error compared to five state-of-the-art approaches across 14 real-word textual data sets covering a wide variety of topics including health, education, business, science and arts.

TL;DR: A new method called semi-supervised ensemble learning guided feature ranking method (SEFR for short), that combines a bagged ensemble of standard semi- supervised approaches with a permutation-based out-of-bag feature importance measure that takes into account both labeled and unlabeled data is proposed.