A scalable saliency-based feature selection method with instance-level information

• Type: Article in International Scientific Journal
• Date: 2020

Authors

Cancela, B (Author) Other; Bolon Canedo, V (Author) Other; Alonso Betanzos, A (Author) Other; João Gama (Author) FEP

Journal

Title: Knowledge-Based Systems

Vol. 192

ISSN: 0950-7051

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 7 Citations

Scopus - 11 Citations

Other information

Authenticus ID: P-00R-H5Y

DOI: 10.1016/j.knosys.2019.105326

Abstract (EN): Classic feature selection techniques remove irrelevant or redundant features to achieve a subset of relevant features in compact models that are easier to interpret and so improve knowledge extraction. Most such techniques operate on the whole dataset, but are unable to provide the user with useful information when only instance-level information is required; in other words, classic feature selection algorithms do not identify the most relevant information in a sample. We have developed a novel feature selection method, called saliency-based feature selection (SFS), based on deep-learning saliency techniques. Our algorithm works under any architecture that is trained by using gradient descent techniques (Neural Networks, SVMs, ...), and can be used for classification or regression problems. Experimental results show our algorithm is robust, as it allows to transfer the feature ranking result between different architectures, achieving remarkable results. The versatility of our algorithm has been also demonstrated, as it can work either in big data environments as well as with small datasets.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13