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.

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.