Medical outcomes are inexorably linked to patient illness and clinical interventions. Interventions change the course of disease, crucially determining outcome. Traditional outcome prediction models build a single classifier by augmenting interventions with disease information. Interventions, however, differentially affect prognosis, thus a single prediction rule may not suffice to capture vari...

We propose a novel approach for credit card fraud detection, which combines evidences from current as well as past behavior. The fraud detection system (FDS) consists of four components, namely, rule-based filter, Dempster–Shafer adder, transaction history database and Bayesian learner. In the rule-based component, we determine the suspicion level of each incoming transaction based on the exten...

Multivariate Bayesian models trained with machine learning, in conjunction with rule-based time-series statistical techniques, are explored for the purpose of improving patient monitoring. Three vital sign data streams and known outcomes for 36 intensive care unit (ICU) patients were captured retrospectively and used to train a set of Bayesian net models and to construct time-series models. Mod...

Decision making under uncertainty is commonly modelled as a process of competitive stochastic evidence accumulation to threshold (the drift-diffusion model). However, it is unknown how animals learn these decision thresholds. We examine threshold learning by constructing a reward function that averages over many trials to Wald’s cost function that defines decision optimality. These rewards are ...

Currently, there is renewed interest in the problem, raised by Shafer in 1985, of updating probabilities when observations are incomplete (or setvalued). This is a fundamental problem, and of articular interest for Bayesian networks. Recently, Griinwald and Halpern have shown that commonly used updating strategies fail here, except under very special assumptions. We propose a new rule for updat...

The rule conflict is an important issue for associative classification due to a large set of rules. In this paper, a new approach called Associative Classification with Bayes (AC-Bayes) is proposed. To address rule conflicts, AC-Bayes has two distinguished features: (1) Associative classification is improved. (2) Naïve Bayesian model is applied in process of classification. A small set of high ...

Schedules can be built in a similar way to a human scheduler by using a set of rules that involve domain knowledge. This paper presents an Estimation of Distribution Algorithm (EDA) for the nurse scheduling problem, which involves choosing a suitable scheduling rule from a set for the assignment of each nurse. Unlike previous work that used Genetic Algorithms (GAs) to implement implicit learnin...

1 I n t r o d u c t i o n The Dempster-Shafer theory is quite popular in knowledge based applications. However, it's exponential computational complexity is a stumbling block. Several researchers worked on the problem of reducing the computational burden of the theory. The work in this direction was initiated by Barnett [1]. The approach of reducing the number of focal elements by certain appro...

Human estimates of the probabilities of combinations of events show well-established violations of probability theory, most notably the conjunction and disjunction fallacies. These violations have led researchers to conclude that the rules of probability are too complex for most people to use, and that cognitively-easier approximations such as averaging are used instead. Unlike previous work th...

This paper concerns a relationship between Bayes’ inference rule and decision rules from the rough set perspective. In statistical inference based on the Bayes’ rule it is assumed that some prior knowledge (prior probability) about some parameters without knowledge about the data is given first. Next the posterior probability is computed by employing the available data. The posterior probabilit...