@InProceedings{Marcílio-JrEler:2020:AsSHVa,
               author = "Marc{\'{\i}}lio-Jr, Wilson Est{\'e}cio and Eler, Danilo 
                         Medeiros",
          affiliation = "S{\~a}o Paulo State University (UNESP) - Department of 
                         Mathematics and Computer Science, Presidente Prudente-SP and 
                         S{\~a}o Paulo State University (UNESP) - Department of 
                         Mathematics and Computer Science, Presidente Prudente-SP",
                title = "From explanations to feature selection: assessing SHAP values as 
                         feature selection mechanism",
            booktitle = "Proceedings...",
                 year = "2020",
               editor = "Musse, Soraia Raupp and Cesar Junior, Roberto Marcondes and 
                         Pelechano, Nuria and Wang, Zhangyang (Atlas)",
         organization = "Conference on Graphics, Patterns and Images, 33. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "feature selection, explainability.",
             abstract = "Explainability has become one of the most discussed topics in 
                         machine learning research in recent years, and although a lot of 
                         methodologies that try to provide explanations to blackbox models 
                         have been proposed to address such an issue, little discussion has 
                         been made on the pre-processing steps involving the pipeline of 
                         development of machine learning solutions, such as feature 
                         selection. In this work, we evaluate a game-theoretic approach 
                         used to explain the output of any machine learning model, SHAP, as 
                         a feature selection mechanism. In the experiments, we show that 
                         besides being able to explain the decisions of a model, it 
                         achieves better results than three commonly used feature selection 
                         algorithms.",
  conference-location = "Porto de Galinhas (virtual)",
      conference-year = "7-10 Nov. 2020",
                  doi = "10.1109/SIBGRAPI51738.2020.00053",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI51738.2020.00053",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/43ALKUL",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/43ALKUL",
           targetfile = "PID6618233.pdf",
        urlaccessdate = "2024, Dec. 02"
}