[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117212-en":3,"doc-seo-117212-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117212,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Fuzzy Machine Learning - A Comprehensive Framework and Systematic Review","Machine learning faces limitations in uncertain environments where observations are imprecise, data are noisy or incomplete, and relationships are ambiguous. Fuzzy machine learning integrates machine learning with fuzzy techniques such as fuzzy sets, fuzzy systems, fuzzy logic, fuzzy measures, and fuzzy relations to handle uncertainty more robustly. A systematic review organizes the field into five categories: fuzzy classical machine learning, fuzzy transfer learning, fuzzy data stream learning, fuzzy reinforcement learning, and fuzzy recommender systems, summarizing recent progress and applications.","Fuzzy Machine Learning: A Comprehensive Framework and Systematic Review  \nJie Lu , Fellow, IEEE, Guangzhi Ma, Student Member, IEEE, and Guangquan Zhang  \n(Survey Paper)  \nAbstract—Machine learning draws its power from various disciplines, including computer science, cognitive science, and statistics. Although machine learning has achieved great advancements in both theory and practice, its methods have some limitations when dealing with complex situations and highly uncertain environments. Insufficient data, imprecise observations, and ambiguous information/relationships can all confound traditional machine learning systems. To address these problems, researchers have integrated machine learning from different aspects and fuzzy techniques, including fuzzy sets, fuzzy systems, fuzzy logic, fuzzy measures, fuzzy relations, and so on. This article presents a systematic review of fuzzy machine learning, from theory, approach to application, with the overall objective of providing an overview of recent achievements in thefield offuzzy machine learning. To this end, the concepts and frameworks discussed are divided into five categories: 1) fuzzy classical machine learning; 2) fuzzy transfer learning; 3) fuzzy data stream learning; 4) fuzzy reinforcement learning; and 5) fuzzy recommender systems. The literature presented should provide researchers with a solid understanding of the current progress in fuzzy machine learning research and its applications.  \nIndex Terms—Data stream learning, fuzzy logic, fuzzy sets and systems, machine learning, recommender systems, transfer learning.  \nI. INTRODUCTION  \npdfifoodaluiinnndgttraomigenpnox, rmdaanedtad varpatinflioteuusrnenssci,entrorivh. Itgavenmagoeeinnh  \nN THE dynamic realm of technology, machine learning has  \ninformation and understand the capabilities of computational systems. However, with most of the existing machine learning methods, accuracy suffers in scenarios characterized by uncertainty, such as the only available observations are imprecise or where the data are noisy or incomplete. In addition, many real-world datasets contain uncertain relationships, and conventional machine learning methods generally find it difficult to  \nManuscript received 4 July 2023; revised 8 January 2024 and 7 March 2024; accepted 1 April 2024 . Date of publication 11 April 2024; date of current version 2 July 2024 . This work was supported by the Australian Research Council under Grant FL190100149 and Grant DP220102635 . Recommended by Associate Editor F. Doctor. (Corresponding author: Jie Lu.)  \nThe authors are with the Australian Artificial Intelligence Institute, Faulty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia (e-mail: [jie.lu@uts.edu.au](jie.lu@uts.edu.au); [guangzhi.ma@](guangzhi.ma@)[ ](guangzhi.ma@)[student.uts.edu.au](student.uts.edu.au); [guangquan.zhang@uts.edu.au](guangquan.zhang@uts.edu.au)).  \nDigital Object Identifier 10.1109/TFUZZ.2024.3387429  \nidentify or work with these structures. To address these issues, researchers have used fuzzy techniques to integrate into machine learning called fuzzy machine learning (FML) [1] as a solution, since fuzzy techniques are successful to deal with uncertainties. FML systems fuse machine learning algorithms with fuzzy techniques, such as fuzzy sets [2], fuzzy systems [3], fuzzy clustering [4], fuzzy relations [5], fuzzy measures [6], fuzzy matching [7], fuzzy optimization [8], and so on, to build new models that are more robust to the many and varied types of uncertainty found in real-world problems.  \nFML stands out as an invaluable ally in the realm of complex and dynamic (uncertain) environments, presenting substantial advantages that elevate its efficacy. Unlike traditional machine learning approaches, fuzzy techniques that are generally based on the concept of fuzzy sets [9] and fuzzy theory [10] excel in capturing and navigating the nuanced shades of uncertainty inherent in dynamic scen","cbCaioRJAhZFwAHk","https://ap.wps.com/l/cbCaioRJAhZFwAHk","pdf",1381843,1,18,"English","en",105,"# Introduction\n## Motivation: uncertainty and limited data\n## Fuzzy machine learning as a solution\n## FML foundations and key techniques\n# Related successes of fuzzy techniques\n## Fuzzy sets for vague, noisy, and interval data\n## Fuzzy-rule-based systems for interpretable prediction\n## Fuzzy clustering for pattern discovery","[{\"question\":\"Why does traditional machine learning struggle in uncertain environments?\",\"answer\":\"When observations are imprecise and data are noisy or incomplete, conventional methods have difficulty capturing uncertain relationships and underlying structures in real-world scenarios.\"},{\"question\":\"What is fuzzy machine learning (FML) and how does it address uncertainty?\",\"answer\":\"FML combines machine learning algorithms with fuzzy techniques such as fuzzy sets, fuzzy systems, fuzzy logic, fuzzy measures, and fuzzy relations to build models that are more robust to various types of uncertainty.\"},{\"question\":\"How does the review classify fuzzy machine learning research?\",\"answer\":\"The survey organizes the literature into five categories: fuzzy classical machine learning, fuzzy transfer learning, fuzzy data stream learning, fuzzy reinforcement learning, and fuzzy recommender systems.\"}]","Fuzzy Machine Learning - 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