Intelligent detection of anomalies in telecommunications customer behaviour

Word processed copy.Includes bibliographical references (leaves 118-121).In this research, we present a modelling technique that can efficiently facilitate anomaly detection that will help call analysts and managers with adaptive decision-making. We developed and implemented a Data 'fransformation System (DTS), a new Hybrid Genetic Algorithm (HGA) and an Anomaly Detection System (ADS) to address this challenge

Probabilistic modeling and reasoning in multiagent decision systems

Ph.DDOCTOR OF PHILOSOPH

Abstract A Novel Algorithm for Scalable and Accurate Bayesian Network Learning

Bayesian Networks (BN) is a knowledge representation formalism that has been proven to be valuable in biomedicine for constructing decision support systems and for generating causal hypotheses from data. Given the emergence of datasets in medicine and biology with thousands of variables and that current algorithms do not scale more than a few hundred variables in practical domains, new efficient and accurate algorithms are needed to learn high quality BNs from data. We present a new algorithm called Max-Min Hill-Climbing (MMHC) that builds upon and improves the Sparse Candidate (SC) algorithm, a state-of-the-art algorithm that scales up to datasets involving hundreds of variables provided the generating networks are sparse. Compared to the SC, on a number of datasets from medicine and biology, (a) MMHC discovers BNs that are structurally closer to the datagenerating BN, (b) the discovered networks are more probable given the data, (c) MMHC is computationally more efficient and scalable than SC, and (d) the generating networks are not required to be sparse. Keywords