Search results for "Decision rule"
showing 10 items of 38 documents
Predictive model to identify the risk of losing protective sensibility of the foot in patients with diabetes mellitus
2019
Diabetic neuropathy is defined as the presence of symptoms and signs of peripheral nerve dysfunction in diabetics. The aim of this study is to develop a predictive logistic model to identify the risk of losing protective sensitivity in the foot. This descriptive cross‐sectional study included 111 patients diagnosed with diabetes mellitus. Participants completed a questionnaire designed to evaluate neuropathic symptoms, and multivariate analysis was subsequently performed to identify an optimal predictive model. The explanatory capacity was evaluated by calculating the R (2) coefficient of Nagelkerke. Predictive capacity was evaluated by calculating sensitivity, specificity, and estimation o…
Regular Minimality and Thurstonian-type modeling
2009
Abstract A Thurstonian-type model for pairwise comparisons is any model in which the response (e.g., “they are the same” or “they are different”) to two stimuli being compared depends, deterministically or probabilistically, on the realizations of two randomly varying representations (perceptual images) of these stimuli. The two perceptual images in such a model may be stochastically interdependent but each has to be selectively dependent on its stimulus. It has been previously shown that all possible discrimination probability functions for same–different comparisons can be generated by Thurstonian-type models of the simplest variety, with independent percepts and deterministic decision ru…
Defining classifier regions for WSD ensembles using word space features
2006
Based on recent evaluation of word sense disambiguation (WSD) systems [10], disambiguation methods have reached a standstill. In [10] we showed that it is possible to predict the best system for target word using word features and that using this 'optimal ensembling method' more accurate WSD ensembles can be built (3-5% over Senseval state of the art systems with the same amount of possible potential remaining). In the interest of developing if more accurate ensembles, w e here define the strong regions for three popular and effective classifiers used for WSD task (Naive Bayes – NB, Support Vector Machine – SVM, Decision Rules – D) using word features (word grain, amount of positive and neg…
Learning vector quantization with alternative distance criteria
2003
An adaptive algorithm for training of a nearest neighbour (NN) classifier is developed in this paper. This learning rule has some similarity to the well-known LVQ method, but uses the nearest centroid neighbourhood concept to estimate optimal locations of the codebook vectors. The aim of this approach is to improve the performance of the standard LVQ algorithms when using a very small codebook. The behaviour of the learning technique proposed here is experimentally compared to those of the plain k-NN decision rule and the LVQ algorithms.
A New Technique of Invariant Statistical Embedding and Averaging in Terms of Pivots for Improvement of Statistical Decisions Under Parametric Uncerta…
2021
In this chapter, a new technique of invariant embedding of sample statistics in a decision criterion (performance index) and averaging this criterion via pivotal quantities (pivots) is proposed for intelligent constructing efficient (optimal, uniformly non-dominated, unbiased, improved) statistical decisions under parametric uncertainty. This technique represents a simple and computationally attractive statistical method based on the constructive use of the invariance principle in mathematical statistics. Unlike the Bayesian approach, the technique of invariant statistical embedding and averaging in terms of pivotal quantities (ISE&APQ) is independent of the choice of priors and represents …
Elements of Significance Testing with Equivalence Problems
1991
AbstractThe paper outlines an approach to the general methodological problem of equivalence assessment which is based on the classical theory of testing statistical hypotheses. Within this frame of reference it is natural to search for decision rules satisfying the same criteria of optimality which are customarily applied in deriving solutions to one- and two-sided testing problems. For three standard situations very frequently encountered in medical applications of statistics, a concise account of such an optimal test for equivalence is presented. It is pointed out that tests based on the well-known principle of confidence interval inclusion are valid in the sense 1 of guaranteeing the pre…
Dangerous relationships : biases in freshwater bioassessment based on observed to expected ratios
2018
Copyright by the Ecological Society of America The ecological assessment of freshwaters is currently primarily based on biological communities and the reference condition approach (RCA). In the RCA, the communities in streams and lakes disturbed by humans are compared with communities in reference conditions with no or minimal anthropogenic influence. The currently favored rationale is using selected community metrics for which the expected values (E) for each site are typically estimated from environmental variables using a predictive model based on the reference data. The proportional differences between the observed values (O) and E are then derived, and the decision rules for status ass…
How to manage the impact of gene flow on oilseed rape grain quality? Simulation case studies of three contrasted landscapes
2012
Abstract Grain admixture due to gene flow of oilseed rape (OSR) could be economically damaging. Different strategies are currently used or proposed to manage grain quality, ranging from homogenisation at the silo level to tactical decision rules at the field level. The relevance of these general strategies was appraised in the case of genetically modified (GM) OSR in three contrasted regions in Europe: Beauce Blesoise (France), Schleswig-Flensburg (Germany), and Fife (UK). Field patterns, crop allocation and agricultural practices were derived from existing datasets and complementary field surveys. Then a gene flow simulator was used to assess how the local contexts influenced the grain adm…
Rough Set Theory for Supporting Decision Making on Relevance in Browsing Multilingual Digital Resources
2017
Browsing digital library (DL) collections seems to pose a challenge for a user owning to the number of factors like for instance, operability of the system, interface readability or clarity, and retrieval efficiency directly related to it, or the number of digital items within the user’s domain. However, when it comes to searching for an item in a foreign language to the user, the number of the factors arises even more which translates proportionally to the growing number of clicks aimed to retrieve the target item. Such a procedure usually leads to disheartening the user from browsing the digital collections. Our study into the user’s behavior interacting with multilingual DL system is set…
Machine learning for mortality analysis in patients with COVID-19
2020
This paper analyzes a sample of patients hospitalized with COVID-19 in the region of Madrid (Spain). Survival analysis, logistic regression, and machine learning techniques (both supervised and unsupervised) are applied to carry out the analysis where the endpoint variable is the reason for hospital discharge (home or deceased). The different methods applied show the importance of variables such as age, O2 saturation at Emergency Rooms (ER), and whether the patient comes from a nursing home. In addition, biclustering is used to globally analyze the patient-drug dataset, extracting segments of patients. We highlight the validity of the classifiers developed to predict the mortality, reaching…