Daniel Xavier de Sousa

Parallel rule‐based selective sampling and on‐demand learning to rank

Thierson Couto, Marcos André Gonçalves, Wellington S. Martins, 2019, Concurr. Comput. Pract. Exp..

Incorporating Risk-Sensitiveness into Feature Selection for Learning to Rank

Thierson Couto, Marcos André Gonçalves, Sérgio D. Canuto, 2016, CIKM.

Risk-Sensitive Learning to Rank with Evolutionary Multi-Objective Feature Selection

Thierson Couto, Marcos André Gonçalves, Sérgio D. Canuto, 2019, ACM Trans. Inf. Syst..

BLAST Distributed Execution on Partitioned Databases with Primary Fragments

Patrick Valduriez, Sérgio Lifschitz, Daniel Xavier de Sousa, 2008, VECPAR.

Improving On-Demand Learning to Rank through Parallelism

Thierson Couto, Marcos André Gonçalves, Wellington Santos Martins, 2012, WISE.

Generalized BROOF-L2R: A General Framework for Learning to Rank Based on Boosting and Random Forests

Marcos André Gonçalves, Thiago Salles, Daniel Xavier de Sousa, 2016, SIGIR.

Individualized extreme dominance (IndED): A new preference-based method for multi-objective recommender systems

Marcos André Gonçalves, Dayanne Gouveia Coelho, Daniel Xavier de Sousa, 2021, Inf. Sci..

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

Marcos André Gonçalves, Sérgio D. Canuto, Sérgio Canuto, 2018, IEEE Transactions on Knowledge and Data Engineering.

Risk-Sensitive Deep Neural Learning to Rank

Daniel Xavier de Sousa, Marcos André Gonçalves, Thierson Couto, 2022, Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.