Interpreting What is Important: An Explainability Approach and Study on Feature Selection

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2023

Autores

Rodrigues, EM (Autor) Outra; Baghoussi, Y (Autor) Outra; João Mendes-Moreira (Autor) FEUP

Ata de Conferência Internacional

Título: PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2023, PT I

Páginas: 288-298

22nd EPIA Conference on Artificial Intelligence (EPIA)

Azores, PORTUGAL, SEP 05-08, 2023

Indexação

ISI Web of Knowledge - 3 Citações

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-00Z-KWC

DOI: 10.1007/978-3-031-49008-8_23

Abstract (EN): Machine learning models are widely used in time series forecasting. One way to reduce its computational cost and increase its efficiency is to select only the relevant exogenous features to be fed into the model. With this intention, a study on the feature selection methods: Pearson correlation coefficient, Boruta, Boruta-Shap, IMV-LSTM, and LIME is performed. A new method focused on interpretability, SHAP-LSTM, is proposed, using a deep learning model training process as part of a feature selection algorithm. The methods were compared in 2 different datasets showing comparable results with lesser computational cost when compared with the use of all features. In all datasets, SHAP-LSTM showed competitive results, having comparatively better results on the data with a higher presence of scarce occurring categorical features.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11