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.
Author(s): Matjaž Kukar (auth.), Honghua Dai, James N. K. Liu, Evgueni Smirnov (eds.)
Edition: 1
Publisher: Springer-Verlag New York
Year: 2012
Language: English
Commentary: Correct bookmarks, cover, pagination
Pages: 310
Tags: Artificial Intelligence (incl. Robotics); Database Management; Pattern Recognition; Data Storage Representation; Computer Graphics
Front Matter....Pages i-xviii
Front Matter....Pages 1-1
Transductive Reliability Estimation for Individual Classifications in Machine Learning and Data Mining....Pages 3-27
Estimating Reliability for Assessing and Correcting Individual Streaming Predictions....Pages 29-49
Error Bars for Polynomial Neural Networks....Pages 51-66
Front Matter....Pages 67-67
Robust-Diagnostic Regression: A Prelude for Inducing Reliable Knowledge from Regression....Pages 69-92
Reliable Graph Discovery....Pages 93-107
Combining Version Spaces and Support Vector Machines for Reliable Classification....Pages 109-126
Reliable Ticket Routing in Expert Networks....Pages 127-147
Reliable Aggregation on Network Traffic for Web Based Knowledge Discovery....Pages 149-159
Sensitivity and Generalization of SVM with Weighted and Reduced Features....Pages 161-182
Reliable Gesture Recognition with Transductive Confidence Machines....Pages 183-200
Front Matter....Pages 201-201
Reliability in A Feature-Selection Process for Intrusion Detection....Pages 203-218
The Impact of Sample Size and Data Quality to Classification Reliability....Pages 219-226
A Comparative Analysis of Instance-based Penalization Techniques for Classification....Pages 227-238
Subsequence Frequency Measurement and its Impact on Reliability of Knowledge Discovery in Single Sequences....Pages 239-255
Front Matter....Pages 257-257
Improving Reliability of Unbalanced Text Mining by Reducing Performance Bias....Pages 259-268
Formal Representation and Verification of Ontology Using State Controlled Coloured Petri Nets....Pages 269-290
A Reliable System Platform for Group Decision Support under Uncertain Environments....Pages 291-306
Back Matter....Pages 307-308
