Reliable Knowledge Discovery
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Beschreibung
Reliable Knowledge Discovery focuses on theory, methods, and techniques for RKDD, a new sub-field of KDD. It studies the theory and methods to assure the reliability and trustworthiness of discovered knowledge and to maintain the stability and consistency of knowledge discovery processes. RKDD has a broad spectrum of applications, especially in critical domains like medicine, finance, and military.
Reliable Knowledge Discovery also presents methods and techniques for designing robust knowledge-discovery processes. Approaches to assessing the reliability of the discovered knowledge are introduced. Particular attention is paid to methods for reliable feature selection, reliable graph discovery, reliable classification, and stream mining. Estimating the data trustworthiness is covered in this volume as well. Case studies are provided in many chapters.
Reliable Knowledge Discovery is designed for researchers and advanced-level students focused on computer science and electrical engineering as a secondary text or reference. Professionals working in this related field and KDD application developers will also find this book useful.
Includes supplementary material: sn.pub/extras
Autor*in
Honghua Dai
Themen in »Reliable Knowledge Discovery«
Reliable knowledge discovery Reliable system platform applications assessing knowledge reliability confidence prediction reliable classification reliable feature selection reliable graph discovery reliable regression reliable stream mining reliable web mining robust knowledge-discovery process