{"title":"机器学习的粒度计算:追求新的发展视野","authors":"Witold Pedrycz","doi":"10.1109/TCYB.2024.3487934","DOIUrl":null,"url":null,"abstract":"Undoubtedly, machine learning (ML) has demonstrated a wealth of far-reaching successes present both at the level of fundamental developments, design methodologies and numerous application areas, quite often encountered in domains requiring a high level of autonomous behavior. Over the passage of time, there are growing challenges of privacy and security, interpretability, explainability, confidence (credibility), and computational sustainability, among others. In this study, we advocate that these quests could be addressed by casting them both conceptually and algorithmically in the unified environment augmented by the principles of granular computing. It is demonstrated that the level of abstraction, delivered by granular computing plays a pivotal role in the interpretation by quantifying the level of credibility of ML constructs. The study also highlights the principles of granular computing and elaborates on its landscape. The original idea of a comprehensive and unified framework of data-knowledge environment of ML is introduced along with a detailed discussion on how data and knowledge are used in a seamless fashion by invoking granular embedding and producing relevant loss functions. Key categories of knowledge-data integration realized at the levels of data and model (involving symbolic/qualitative models and physics-oriented models) and investigated.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 1","pages":"460-471"},"PeriodicalIF":9.4000,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Granular Computing for Machine Learning: Pursuing New Development Horizons\",\"authors\":\"Witold Pedrycz\",\"doi\":\"10.1109/TCYB.2024.3487934\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Undoubtedly, machine learning (ML) has demonstrated a wealth of far-reaching successes present both at the level of fundamental developments, design methodologies and numerous application areas, quite often encountered in domains requiring a high level of autonomous behavior. Over the passage of time, there are growing challenges of privacy and security, interpretability, explainability, confidence (credibility), and computational sustainability, among others. In this study, we advocate that these quests could be addressed by casting them both conceptually and algorithmically in the unified environment augmented by the principles of granular computing. It is demonstrated that the level of abstraction, delivered by granular computing plays a pivotal role in the interpretation by quantifying the level of credibility of ML constructs. The study also highlights the principles of granular computing and elaborates on its landscape. The original idea of a comprehensive and unified framework of data-knowledge environment of ML is introduced along with a detailed discussion on how data and knowledge are used in a seamless fashion by invoking granular embedding and producing relevant loss functions. Key categories of knowledge-data integration realized at the levels of data and model (involving symbolic/qualitative models and physics-oriented models) and investigated.\",\"PeriodicalId\":13112,\"journal\":{\"name\":\"IEEE Transactions on Cybernetics\",\"volume\":\"55 1\",\"pages\":\"460-471\"},\"PeriodicalIF\":9.4000,\"publicationDate\":\"2024-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Cybernetics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10756208/\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Cybernetics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10756208/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Granular Computing for Machine Learning: Pursuing New Development Horizons
Undoubtedly, machine learning (ML) has demonstrated a wealth of far-reaching successes present both at the level of fundamental developments, design methodologies and numerous application areas, quite often encountered in domains requiring a high level of autonomous behavior. Over the passage of time, there are growing challenges of privacy and security, interpretability, explainability, confidence (credibility), and computational sustainability, among others. In this study, we advocate that these quests could be addressed by casting them both conceptually and algorithmically in the unified environment augmented by the principles of granular computing. It is demonstrated that the level of abstraction, delivered by granular computing plays a pivotal role in the interpretation by quantifying the level of credibility of ML constructs. The study also highlights the principles of granular computing and elaborates on its landscape. The original idea of a comprehensive and unified framework of data-knowledge environment of ML is introduced along with a detailed discussion on how data and knowledge are used in a seamless fashion by invoking granular embedding and producing relevant loss functions. Key categories of knowledge-data integration realized at the levels of data and model (involving symbolic/qualitative models and physics-oriented models) and investigated.
期刊介绍:
The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.