NCC-EM: A hybrid framework for decision making with missing information

Title from PDF of title page viewed January 30, 2018Thesis advisor: Chen ZhiQiangVitaIncludes bibliographical references (pages 43-46)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2017Accounting for uncertainty is important in any data driven decision making. The popular treatment of uncertainties is to employ classical probability theory by expressing variables as random variables or processes in terms of random distributions. This precise approach encounters difficulty and leads to deceptive predictions when the sources of uncertainty are epistemic in terms of incomplete (missing), conflicting, or erroneous information due to the lack of knowledge. There have been many frameworks developed against the precise probability formalism, and one of such frameworks is the Imprecise Probability (IP) based modeling. In this thesis, we develop and provide a novel hybrid framework, Naïve Credal Classifier with Expectation-Maximization data imputation, for decision making with missing information. The IP-based Credal Set concept is first introduced to model uncertainties for data with missing information. Then the Naïve Credal Classifier (NCC) is employed in this work, which is provided by the latest JNCC2 package. The key idea and research findings in this research is to model missing data using advanced imputation techniques to minimize the performance (accuracy) loss in NCC. The resulting NCC-EM framework is hybrid where the EM imputation technique is used as a preprocessing step. To verify and validate this hybrid framework, the NCC-EM is extensively tested on open machine learning datasets by simulating missing values, and it is shown that NCC-EM outperforms the existing NCC framework and traditional supervised classification methods.Introduction -- introduction to imprecise probability -- Naïve Bayes Classifier and Naïve Credal classifier -- NCC-EM: a novel Credal based framework -- Conclusion and future wor

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes

Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998

Evaluating Microarray-based Classifiers: An Overview

For the last eight years, microarray-based class prediction has been the subject of numerous publications in medicine, bioinformatics and statistics journals. However, in many articles, the assessment of classification accuracy is carried out using suboptimal procedures and is not paid much attention. In this paper, we carefully review various statistical aspects of classifier evaluation and validation from a practical point of view. The main topics addressed are accuracy measures, error rate estimation procedures, variable selection, choice of classifiers and validation strategy

An open toolbox for the reduction, inference computation and sensitivity analysis of Credal Networks

Bayesian Networks are a flexible and intuitive tool associated with a robust mathematical background. They have attracted increasing interest in a large variety of applications in different fields. In spite of this, inference in traditional Bayesian Networks is generally limited to only discrete variables or to probabilistic distributions (adopting approximate inference algorithms) that cannot fully capture the epistemic imprecision of the data available. In order to overcome these limitations, Credal Networks have been proposed to integrate Bayesian Networks with imprecise probabilities which, adopting non-probabilistic or hybrid models, allow to fully represent the information available and its uncertainty. Here, a novel computational tool, implemented in the general purpose software OpenCossan, is proposed. The tool provides the reduction of Credal Networks through the use of structural reliability methods, in order to limit the cost associated with the inference computation without impoverishing the quality of the information initially introduced. Novel algorithms for the inference computation of networks involving probability bounds are provided. In addition, a novel sensitivity approach is proposed and implemented into the Toolbox in order to identify the maximum tolerable uncertainty associated with the inputs

ISIPTA'07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications

B

Sequential Decision Making For Choice Functions On Gambles

Choice functions on gambles (uncertain rewards) provide a framework for studying diverse preference and uncertainty models. For single decisions, applying a choice function is straightforward. In sequential problems, where the subject has multiple decision points, it is less easy. One possibility, called a normal form solution, is to list all available strategies (specifications of acts to take in all eventualities). This reduces the problem to a single choice between gambles. We primarily investigate three appealing behaviours of these solutions. The first, subtree perfectness, requires that the solution of a sequential problem, when restricted to a sub-problem, yields the solution to that sub-problem. The second, backward induction, requires that the solution of the problem can be found by working backwards from the final stage of the problem, removing everything judged non-optimal at any stage. The third, locality, applies only to special problems such as Markov decision processes, and requires that the optimal choice at each stage (considered separately from the rest of the problem) forms an optimal strategy. For these behaviours, we find necessary and sufficient conditions on the choice function. Showing that these hold is much easier than proving the behaviour from first principles. It also leads to answers to related questions, such as the relationship between the normal form and another popular form of solution, the extensive form. To demonstrate how these properties can be checked for particular choice functions, and how the theory can be easily extended to special cases, we investigate common choice functions from the theory of coherent lower previsions

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen