121 research outputs found
Preference mining techniques for customer behavior analysis
The thesis has studied a number of critical problems in data mining for customer behavior analysis and has proposed novel techniques for better modeling of the customers’ decision making process, more efficient analysis of their travel behavior, and more effective identification of their emerging preference
Intelligent Systems
This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier
Coevolutionary fuzzy modeling
This thesis presents Fuzzy CoCo, a novel approach for system design, conducive to explaining human decisions. Based on fuzzy logic and coevolutionary computation, Fuzzy CoCo is a methodology for constructing systems able to accurately predict the outcome of a human decision-making process, while providing an understandable explanation of the underlying reasoning. Fuzzy logic provides a formal framework for constructing systems exhibiting both good numeric performance (precision) and linguistic representation (interpretability). From a numeric point of view, fuzzy systems exhibit nonlinear behavior and can handle imprecise and incomplete information. Linguistically, they represent knowledge in the form of rules, a natural way for explaining decision processes. Fuzzy modeling —meaning the construction of fuzzy systems— is an arduous task, demanding the identification of many parameters. This thesis analyses the fuzzy-modeling problem and different approaches to coping with it, focusing on evolutionary fuzzy modeling —the design of fuzzy inference systems using evolutionary algorithms— which constitutes the methodological base of my approach. In order to promote this analysis the parameters of a fuzzy system are classified into four categories: logic, structural, connective, and operational. The central contribution of this work is the use of an advanced evolutionary technique —cooperative coevolution— for dealing with the simultaneous design of connective and operational parameters. Cooperative coevolutionary fuzzy modeling succeeds in overcoming several limitations exhibited by other standard evolutionary approaches: stagnation, convergence to local optima, and computational costliness. Designing interpretable systems is a prime goal of my approach, which I study thoroughly herein. Based on a set of semantic and syntactic criteria, regarding the definition of linguistic concepts and their causal connections, I propose a number of strategies for producing more interpretable fuzzy systems. These strategies are implemented in Fuzzy CoCo, resulting in a modeling methodology providing high numeric precision, while incurring as little a loss of interpretability as possible. After testing Fuzzy CoCo on a benchmark problem —Fisher's Iris data— I successfully apply the algorithm to model the decision processes involved in two breast-cancer diagnostic problems: the WBCD problem and the Catalonia mammography interpretation problem. For the WBCD problem, Fuzzy CoCo produces systems both of high performance and high interpretability, comparable (if not better) than the best systems demonstrated to date. For the Catalonia problem, an evolved high-performance system was embedded within a web-based tool —called COBRA— for aiding radiologists in mammography interpretation. Several aspects of Fuzzy CoCo are thoroughly analyzed to provide a deeper understanding of the method. These analyses show the consistency of the results. They also help derive a stepwise guide to applying Fuzzy CoCo, and a set of qualitative relationships between some of its parameters that facilitate setting up the algorithm. Finally, this work proposes and explores preliminarily two extensions to the method: Island Fuzzy CoCo and Incremental Fuzzy CoCo, which together with the original CoCo constitute a family of coevolutionary fuzzy modeling techniques. The aim of these extensions is to guide the choice of an adequate number of rules for a given problem. While Island Fuzzy CoCo performs an extended search over different problem sizes, Incremental Fuzzy CoCo bases its search power on a mechanism of incremental evolution
Biomedical applications of belief networks
Biomedicine is an area in which computers have long been expected to play a significant
role. Although many of the early claims have proved unrealistic, computers are gradually
becoming accepted in the biomedical, clinical and research environment. Within these
application areas, expert systems appear to have met with the most resistance, especially
when applied to image interpretation.In order to improve the acceptance of computerised decision support systems it is
necessary to provide the information needed to make rational judgements concerning
the inferences the system has made. This entails an explanation of what inferences
were made, how the inferences were made and how the results of the inference are to
be interpreted. Furthermore there must be a consistent approach to the combining of
information from low level computational processes through to high level expert analyses.nformation from low level computational processes through to high level expert analyses.
Until recently ad hoc formalisms were seen as the only tractable approach to reasoning
under uncertainty. A review of some of these formalisms suggests that they are less
than ideal for the purposes of decision making. Belief networks provide a tractable way
of utilising probability theory as an inference formalism by combining the theoretical
consistency of probability for inference and decision making, with the ability to use the
knowledge of domain experts.nowledge of domain experts.
The potential of belief networks in biomedical applications has already been recog¬
nised and there has been substantial research into the use of belief networks for medical
diagnosis and methods for handling large, interconnected networks. In this thesis the use
of belief networks is extended to include detailed image model matching to show how,
in principle, feature measurement can be undertaken in a fully probabilistic way. The
belief networks employed are usually cyclic and have strong influences between adjacent
nodes, so new techniques for probabilistic updating based on a model of the matching
process have been developed.An object-orientated inference shell called FLAPNet has been implemented and used
to apply the belief network formalism to two application domains. The first application is
model-based matching in fetal ultrasound images. The imaging modality and biological
variation in the subject make model matching a highly uncertain process. A dynamic,
deformable model, similar to active contour models, is used. A belief network combines
constraints derived from local evidence in the image, with global constraints derived from
trained models, to control the iterative refinement of an initial model cue.In the second application a belief network is used for the incremental aggregation of
evidence occurring during the classification of objects on a cervical smear slide as part of
an automated pre-screening system. A belief network provides both an explicit domain
model and a mechanism for the incremental aggregation of evidence, two attributes
important in pre-screening systems.Overall it is argued that belief networks combine the necessary quantitative features
required of a decision support system with desirable qualitative features that will lead
to improved acceptability of expert systems in the biomedical domain
- …