{"type":"ScholarlyArticle","title":"Personal Context Recognition via Skeptical Learning","authors":[{"name":"Wanyi Zhang","slug":"wanyi-zhang","given":"Wanyi","family":"Zhang","affiliation":"DISI , University of Trento , Italy"}],"abstract":"In personal context recognition many solutions rely on supervised learning that uses sensor data collected from the users' mobile devices. However, the recognition performance is significantly affected by the annotations’ quality. The problem lies in the fact that the annotator in such scenarios is usually the user herself which is not an expert and thus provides a significant amount of incorrect labels, while existing solutions can only tolerate a small fraction of mislabels. Our solution is Skeptical Learning, a framework for interactive machine learning where the machine uses all its available knowledge to check the correctness of its own and the user labeling. This allows to have a uniform confidence measure to be used when a contradiction arises that applies to both the annotator and the machine. The criteria of success is an improvement of the final recognition accuracy with respect to traditional supervised approaches.","doi":"10.24963/ijcai.2019/930","url":"https://ijcai.org/proceedings/2019/930","pdf":"https://ijcai.org/proceedings/2019/0930.pdf","markdown":"https://ijcai.org/proceedings/2019/930.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/930","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":930,"pages":{"first":6482,"last":6483},"keywords":["Machine Learning: Knowledge-based Learning","Humans and AI: Human-AI Collaboration"],"videos":[],"references":[{"position":1,"raw":"[Frénay and Verleysen, 2014] Benoît Frénay and Michel Verleysen. Classification in the presence of label noise: a survey. IEEE transactions on neural networks and learn- ing systems, 25(5):845-869, 2014.","title":"Classification in the presence of label noise: a survey","authors":"Verleysen Frénay","year":"2014","venue":"IEEE transactions on neural networks and learning systems","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Giunchiglia et al., 2017a] Fausto Giunchiglia, Khuyag- baatar Batsuren, and Gabor Bella. Understanding and exploiting language diversity. In IJCAI, pages 4009-4017, 2017.","title":"Understanding and exploiting language diversity","authors":"Giunchiglia","year":"2017","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2017/560"},{"position":3,"raw":"[Giunchiglia et al., 2017b] Fausto Giunchiglia, Enrico Big- notti, and Mattia Zeni. Personal context modelling and annotation. In Pervasive Computing and Communica- tions Workshops (PerCom Workshops), 2017 IEEE Inter- national Conference on, pages 117-122. IEEE, 2017.","title":"Personal context modelling and annotation","authors":"Giunchiglia","year":"2017","venue":"Pervasive Computing and Communications Workshops (PerCom Workshops), 2017 IEEE International Conference on","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[West and Sinibaldi, 2013] Brady T West and Jennifer Sini- baldi. The quality of paradata: A literature review. Im- proving Surveys with Paradata, pages 339-359, 2013.","title":"The quality of paradata: A literature review. Improving Surveys with Paradata","authors":"T Brady, Jennifer West, Sinibaldi","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Zeni et al., 2019] Mattia Zeni, Wanyi Zhang, Enrico Big- notti, Andrea Passerini, and Fausto Giunchiglia. Fixing mislabeling by human annotators leveraging conflict reso- lution and prior knowledge. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technolo- gies, 3(1):32, 2019.","title":"Fixing mislabeling by human annotators leveraging conflict resolution and prior knowledge","authors":"Mattia Zeni, Wanyi Zhang, Enrico Bignotti, Andrea Passerini, Fausto Giunchiglia","year":"2019","venue":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}