{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multiple-instance-choquet-integral-classifier","title":"Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications","arxiv_id":"1803.04048","date":"2018-03-11","proceeding":null,"authors":["Xiaoxiao Du","Alina Zare"],"abstract":"In classifier (or regression) fusion the aim is to combine the outputs of\nseveral algorithms to boost overall performance. Standard supervised fusion\nalgorithms often require accurate and precise training labels. However,\naccurate labels may be difficult to obtain in many remote sensing applications.\nThis paper proposes novel classification and regression fusion models that can\nbe trained given ambiguosly and imprecisely labeled training data in which\ntraining labels are associated with sets of data points (i.e., \"bags\") instead\nof individual data points (i.e., \"instances\") following a multiple instance\nlearning framework. Experiments were conducted based on the proposed algorithms\non both synthetic data and applications such as target detection and crop yield\nprediction given remote sensing data. The proposed algorithms show effective\nclassification and regression performance.","url_abs":"http://arxiv.org/abs/1803.04048v2","url_pdf":"http://arxiv.org/pdf/1803.04048v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multiple-instance-choquet-integral-classifier","repo_url":"https://github.com/GatorSense/MICI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"multiple-instance-choquet-integral-classifier","repo_url":"https://github.com/hvak/bi-michi","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"multiple-instance-choquet-integral-classifier","repo_url":"https://github.com/hvak/mimrf-bfm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"crop-yield-prediction","task_name":"Crop Yield Prediction"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multiple-instance-learning","task_name":"Multiple Instance Learning"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}