Search results for "DATA MINING"

Correlations of isokinetic and psychophysical back lift and static back extensor endurance tests in men

1994

Isokinetic lift, psychophysical lift, and static back endurance tests are among the most widely used measurements of muscle function for determining risk for, and recovery from, back problems. After determining test repeatability we examined the degree of association between these test measures in 100 men selected to represent a variety of occupations and lifestyles. Isokinetic lifting had low correlations with test results for psychophysical lifting (r = 0.28) and static back endurance (r = 0.24). Static back endurance and psychophysical lift test results were not at all correlated (r = 0.03). RELEVANCE--:Muscle function measurements for back problems are sometimes arbitrarily selected due…

Modeling user preferences in content-based image retrieval: A novel attempt to bridge the semantic gap

2015

This paper is concerned with content-based image retrieval from a stochastic point of view. The semantic gap problem is addressed in two ways. First, a dimensional reduction is applied using the (pre-calculated) distances among images. The dimension of the reduced vector is the number of preferences that we allow the user to choose from, in this case, three levels. Second, the conditional probability distribution of the random user preference, given this reduced feature vector, is modeled using a proportional odds model. A new model is fitted at each iteration. The score used to rank the image database is based on the estimated probability function of the random preference. Additionally, so…

A principled approach to network-based classification and data representation

2013

Measures of similarity are fundamental in pattern recognition and data mining. Typically the Euclidean metric is used in this context, weighting all variables equally and therefore assuming equal relevance, which is very rare in real applications. In contrast, given an estimate of a conditional density function, the Fisher information calculated in primary data space implicitly measures the relevance of variables in a principled way by reference to auxiliary data such as class labels. This paper proposes a framework that uses a distance metric based on Fisher information to construct similarity networks that achieve a more informative and principled representation of data. The framework ena…

Domain knowledge integration and semantical quality management -A biology case study

2008

International audience; The management of semantical quality is a major challenge in the context of knowledge integration. In this paper, we describe a new approach to constraint management that emphasizes constraint traceability when moving from the semantical level to the operational one.Our strategy for management of semantical quality is related to a metamo-deling-based approach to knowledge integration. We carry out knowledge integration “on the fly” by using transformations applied to models belonging to our metamodeling architecture. The resulting integrated models access available resources through web services whose input and output parameters are guarded by constraints. Integrated…

Arbiter Meta-Learning with Dynamic Selection of Classifiers and its Experimental Investigation

1999

In data mining, the selection of an appropriate classifier to estimate the value of an unknown attribute for a new instance has an essential impact to the quality of the classification result. Recently promising approaches using parallel and distributed computing have been presented. In this paper, we consider an approach that uses classifiers trained on a number of data subsets in parallel as in the arbiter meta-learning technique. We suggest that information is collected during the learning phase about the performance of the included base classifiers and arbiters and that this information is used during the application phase to select the best classifier dynamically. We evaluate our techn…

Text Classification Using Novel “Anti-Bayesian” Techniques

2015

This paper presents a non-traditional “Anti-Bayesian” solution for the traditional Text Classification (TC) problem. Historically, all the recorded TC schemes work using the fundamental paradigm that once the statistical features are inferred from the syntactic/semantic indicators, the classifiers themselves are the well-established statistical ones. In this paper, we shall demonstrate that by virtue of the skewed distributions of the features, one could advantageously work with information latent in certain “non-central” quantiles (i.e., those distant from the mean) of the distributions. We, indeed, demonstrate that such classifiers exist and are attainable, and show that the design and im…

Predictive and Contextual Feature Separation for Bayesian Metanetworks

2007

Bayesian Networks are proven to be a comprehensive model to describe causal relationships among domain attributes with probabilistic measure of conditional dependency. However, depending on a context, many attributes of the model might not be relevant. If a Bayesian Network has been learned across multiple contexts then all uncovered conditional dependencies are averaged over all contexts and cannot guarantee high predictive accuracy when applied to a concrete case. We are considering a context as a set of contextual attributes, which are not directly effect probability distribution of the target attributes, but they effect on "relevance" of the predictive attributes towards target attribut…

Comparative evaluation of data preprocessing software tools to increase efficiency and accuracy in diffusion kurtosis imaging

2016

Do we need metamodels AND ontologies for engineering platforms?

2006

In this paper we show how the joint use of metamodeling and ontologies allows to describe domain knowledge for a complex domain. Ontologies are used as stabilized descriptions of a business domain while metamodels allow a fine description of the domain (to be constructed in the initial phases of modeling). We propose to use an ontology for early categorization, i.e., as a "natural" complement of the formal system that is induced by the metamodel.

A Pattern Recognition Approach for Peak Prediction of Electrical Consumption

2014

Predicting and mitigating demand peaks in electrical networks has become a prevalent research topic. Demand peaks pose a particular challenge to energy companies because these are difficult to foresee and require the net to support abnormally high consumption levels. In smart energy grids, time-differentiated pricing policies that increase the energy cost for the consumers during peak periods, and load balancing are examples of simple techniques for peak regulation. In this paper, we tackle the task of predicting power peaks prior to their actual occurrence in the context of a pilot Norwegian smart grid network.