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

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Rodrigues, EM (Author) Other; Baghoussi, Y (Author) Other; João Mendes-Moreira (Author) FEUP

Conference proceedings International

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

Pages: 288-298

22nd EPIA Conference on Artificial Intelligence (EPIA)

Azores, PORTUGAL, SEP 05-08, 2023

Indexing

ISI Web of Knowledge - 3 Citations

Scopus - 3 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11