Transductive Reliability Estimation for Individual Classifications in Machine Learning and Data Mining.- Estimating Reliability for Assessing and Correcting Individual Streaming Predictions.- Error Bars for Polynomial Neural Networks.- Robust-Diagnostic Regression: A Prelude for Inducing Reliable Knowledge from Regression.- Reliable Graph Discovery.- Combining Version Spaces and Support Vector Machines for Reliable Classification.- Reliable Ticket Routing in Expert Networks.- Reliable Aggregation on Network Traffic for Web Based Knowledge Discovery.- Sensitivity and Generalization of SVM with Weighted and Reduced Features.- Reliable Gesture Recognition with Transductivie Confidence Machines.- Reliability in A Feature-Selection Process for Intrusion Detection.- The Impact of Sample Size and Data Quality to Classification Reliability.- A Comparative Analysis of Instance-based Penalization Techniques for Classification.- Subsequence Frequency Measurement and its Impact on Reliability of Knowledge Discovery in Single Sequences.- Improving Reliability of Unbalanced Text Mining by Reducing Performance Bias.- Formal Representation and Verification of Ontology Using State Controlled Coloured Petri Nets.- A Reliable System Platform for Group Decision Support under Uncertain Environments.- Index.
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.