{"type":"ScholarlyArticle","title":"Context-Aware Feature Selection and Classification","authors":[{"name":"Juanyan Wang","slug":"juanyan-wang","given":"Juanyan","family":"Wang","affiliation":"Illinois Institute of Technology , Chicago , IL , USA"},{"name":"Mustafa Bilgic","slug":"mustafa-bilgic","given":"Mustafa","family":"Bilgic","affiliation":"Illinois Institute of Technology , Chicago , IL , USA"}],"abstract":"We propose a joint model that performs instance-level feature selection and classification. For a given case, the joint model first skims the full feature vector, decides which features are relevant for that case, and makes a classification decision using only the selected features, resulting in compact, interpretable, and case-specific classification decisions. Because the selected features depend on the case at hand, we refer to this approach as context-aware feature selection and classification. The model can be trained on instances that are annotated by experts with both class labels and instance-level feature selections, so it can select instance-level features that humans would use. Experiments on several datasets demonstrate that the proposed model outperforms eight baselines on a combined classification and feature selection measure, and is able to better emulate the ground-truth instance-level feature selections. The supplementary materials are available at https://github.com/IIT-ML/IJCAI23-CFSC.","doi":"10.24963/ijcai.2023/480","url":"https://ijcai.org/proceedings/2023/480","pdf":"https://ijcai.org/proceedings/2023/0480.pdf","markdown":"https://ijcai.org/proceedings/2023/480.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/480","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":480,"pages":{"first":4317,"last":4325},"keywords":["Machine Learning: ML: Feature extraction, selection and dimensionality reduction","Machine Learning: ML: Classification","Machine Learning: ML: Explainable/Interpretable machine learning"],"videos":[],"references":[{"position":1,"raw":"[Adriaenssens et al., 2004] Veronique Adriaenssens, Bernard De Baets, Peter LM Goethals, and Niels De Pauw. 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