Competitive exception learning using fuzzy frequency distributions

A competitive exception learning algorithm for finding a non-linear mapping is proposed which puts the emphasis on the discovery of the important exceptions rather than the main rules. To do so,we first cluster the output space using a competitive fuzzy clustering algorithm and derive a fuzzy frequency distribution describing the general, average system's output behavior. Next, we look for a fuzzy partitioning of the input space in such away that the corresponding fuzzy output frequency distributions `deviate at most' from the average one as found in the first step. In this way, the most important `exceptional regions' in the input-output relation are determined. Using the joint input-output fuzzy frequency distributions, the complete input-output function as extracted from the data, can be expressed mathematically. In addition, the exceptions encountered can be collected and described as a set of fuzzy if-then-else-rules. Besides presenting a theoretical description of the new exception learning algorithm, we report on the outcomes of certain practical simulations

Licorice consumption as a cause of posterior reversible encephalopathy syndrome: a case report

INTRODUCTION: A 49-year-old woman was admitted to our hospital because of thunderclap headache and blurred vision. At the time of presentation, her blood pressure was 219/100 mmHg, her arterial pH was 7.64 and her potassium level was 2.7 mM/l. METHODS: The combination of sequential computed tomography (CT) and the triad of hypertension, hypokalemia and metabolic alkalosis in this patient suggested the diagnosis. Supplementary anamnesis and long-term follow-up confirmed it. RESULTS: Brain computed tomography imaging showed minor bleeding in the left Sylvian fissure and bilateral occipital edema, suggestive of posterior reversible encephalopathy syndrome (PRES). Repeated brain CT after 10 days showed a complete resolution of radiological signs. The patient informed us that she had quit smoking 2 weeks ago and had started consuming large amounts of licorice instead of smoking. After she abandoned licorice consumption, her blood pressure normalized. Her latest blood pressure reading was 106/60 mmHg without the use of any antihypertensive drugs. CONCLUSIONS: To the best of our knowledge, this is the first case report describing licorice consumption as a cause of PRES. Glycyrrhizic acid, a component of licorice, inhibits 11β-hydroxysteroid dehydrogenase and subsequently causes mineralocorticoid excess. Mineralocorticoid excess in turn causes high blood pressure and ultimately gives rise to malignant hypertension. Physicians should remember that licorice use is a very easy-to-treat cause of hypertension, hypertensive encephalopathy and PRES

Financial Markets Analysis by Probabilistic Fuzzy Modelling

For successful trading in financial markets, it is important to develop financial models where one can identify different states of the market for modifying one???s actions. In this paper, we propose to use probabilistic fuzzy systems for this purpose. We concentrate on Takagi???Sugeno (TS) probabilistic fuzzy systems that combine interpretability of fuzzy systems with the statistical properties of probabilistic systems. We start by recapitulating the general architecture of TS probabilistic fuzzy rule-based systems and summarize the corresponding reasoning schemes. We mention how probabilities can be estimated from a given data set and how a probability distribution can be approximated by a fuzzy histogram. We apply our methodology for financial time series analysis and demonstrate how a probabilistic TS fuzzy system can be identified, assuming that a linguistic term set is given. We illustrate the interpretability of such a system by inspecting the rule bases of our models

Relative Distress and Return Distribution Characteristics of Japanese Stocks, a Fuzzy-Probabilistic Approach

In this article, we demonstrate that a direct relation exists between the context of Japanese firms indicating relative distress and conditional return distribution properties. We map cross-sectional vectors with company characteristics on vectors with return feature vectors, using a fuzzy identification technique called Competitive Exception Learning Algorithm (CELA)1. In this study we use company characteristics that follow from capital structure theory and we relate the recognized conditional return properties to this theory. Using the rules identified by this mapping procedure this approach enables us to make conditional predictions regarding the probability of a stock's or a group of stocks' return series for different return distribution classes (actually return indices). Using these findings, one may construct conditional indices that may serve as benchmarks. These would be particularly useful for tracking and portfolio management

CASSIS: a Modeling Language for Customizable User Interface Designs

Abstract. Current user interface modeling languages usually focus on modeling a single user interface and have a fixed set of user interface components; adding another user interface component requires an extension of the language. In this paper we present CASSIS, a concise language that supports creation of user interface components using models instead of language extensions. It also allows the specification of design-time and runtime user interface variations. The support for variations has been used to generate constraints for custom user interface components, to specify design patterns and design decisions. CASSIS has been used in several projects including a multi-disciplinary applied research project

Probabilistic and Statistical Fuzzy Set Foundations of Competitive Exception Learning

Recently, a Competitive Exception Learning Algorithm (CELA) was introduced [1, 2]. This algorithm establishes an optimal mapping from a (continuous) M-dimensional input sample space to an N-dime

Transforming protein engineering: advanced integration of deep learning and 3DM technology for superior protein function predictions

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Early stages of building a rare disease registry, methods and 2010 data from the Belgian Neuromuscular Disease Registry (BNMDR)

The Belgian Neuromuscular Disease Registry, commissioned in 2008, aims to collect data to improve knowledge on neuromuscular diseases and enhance quality health services for neuromuscular disease patients. This paper presents a clear outline of the strategy to launch a global national registry. All patients diagnosed with one of the predefined 62 neuromuscular disease groups and living in Belgium may be included in the yearly updated Registry. Basic core data is harvested through a newly designed web application by the six accredited neuromuscular reference centres. In 2010, 3,424 patients with a neuromuscular disorder were registered. The most prevalent disease group in the Registry is Hereditary Motor and Sensory Neuropathy, as similarly stated by other studies, albeit the prevalence in Belgium is five times lower: 6.5 per 100,000 in the north of Belgium, versus 17.0-41.0 per 100,000 in other areas of Europe. Very few patients were captured in the south of the country. With the aim to collect valuable epidemiological data, the registry targets to gather high quality data, that the sample to be representative of the population and that it be complete. The past 5 years of building the registry have improved its quality, albeit the consistent gap in data from the south of the country prevails, influencing the estimated prevalence of these diseases. To this day, the true burden of neuromuscular diseases in Belgium is not known but actions have been undertaken to address these issues

A tutorial on Bayesian single-test reliability analysis with JASP

The current practice of reliability analysis is both uniform and troublesome: most reports consider only Cronbach’s α, and almost all reports focus exclusively on a point estimate, disregarding the impact of sampling error. In an attempt to improve the status quo we have implemented Bayesian estimation routines for five popular single-test reliability coefficients in the open-source statistical software program JASP. Using JASP, researchers can easily obtain Bayesian credible intervals to indicate a range of plausible values and thereby quantify the precision of the point estimate. In addition, researchers may use the posterior distribution of the reliability coefficients to address practically relevant questions such as “What is the probability that the reliability of my test is larger than a threshold value of .80?”. In this tutorial article, we outline how to conduct a Bayesian reliability analysis in JASP and correctly interpret the results. By making available a computationally complex procedure in an easy-to-use software package, we hope to motivate researchers to include uncertainty estimates whenever reporting the results of a single-test reliability analysis