Search results for "Data mining"
showing 10 items of 907 documents
Co-citation Percentile Rank and JYUcite : a new network-standardized output-level citation influence metric and its implementation using Dimensions A…
2022
AbstractJudging value of scholarly outputs quantitatively remains a difficult but unavoidable challenge. Most of the proposed solutions suffer from three fundamental shortcomings: they involve (i) the concept of journal, in one way or another, (ii) calculating arithmetic averages from extremely skewed distributions, and (iii) binning data by calendar year. Here, we introduce a new metric Co-citation Percentile Rank (CPR), that relates the current citation rate of the target output taken at resolution of days since first citable, to the distribution of current citation rates of outputs in its co-citation set, as its percentile rank in that set. We explore some of its properties with an examp…
Bayesian metanetworks for modelling user preferences in mobile environment
2003
The problem of profiling and filtering is important particularly for mobile information systems where wireless network traffic and mobile terminal’s size are limited comparing to the Internet access from the PC. Dealing with uncertainty in this area is crucial and many researchers apply various probabilistic models. The main challenge of this paper is the multilevel probabilistic model (the Bayesian Metanetwork), which is an extension of traditional Bayesian networks. The extra level(s) in the Metanetwork is used to select the appropriate substructure from the basic network level based on contextual features from user’s profile (e.g. user’s location). Two models of the Metanetwork are consi…
From fractal urban pattern analysis to fractal urban planning concepts
2014
International audience; Fractal geometry can be used to develop a multiscale approach toinvestigate the spatial organization of urban fabrics. First, the concepts behindfractal reference models are introduced so as to provide a better understandingof the results obtained from empirical analyses of urban patterns. Then, differentmethods for conducting fractal analyses are presented and the results obtained forurban patterns are discussed. It turns out that, despite their irregular appearance,urban patterns are often organized by an inherent fractal order principle, at leastacross a certain range of scales. More detailed analysis of the findings reveals linksbetween these fractal properties a…
Bayesian Metanetwork for Context-Sensitive Feature Relevance
2006
Bayesian Networks are proven to be a comprehensive model to describe causal relationships among domain attributes with probabilistic measure of appropriate conditional dependency. However, depending on task and context, many attributes of the model might not be relevant. If a 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 a “relevance” of the predictive attributes towards tar…
The CogALex-IV Shared Task on the Lexical Access Problem
2014
The shared task of the 4th Workshop on Cognitive Aspects of the Lexicon (CogALexIV) was devoted to a subtask of the lexical access problem, namely multi-stimulus association. In this task, participants were supposed to determine automatically an expected response based on a number of received stimulus words. We describe here the task definition, the theoretical background, the training and test data sets, and the evaluation procedure used for ranking the participating systems. We also summarize the approaches used and present the results of the evaluation. In conclusion, the outcome of the competition are a number of systems which provide very good solutions to the problem.
Using proximity and spatial homogeneity in neighbourhood-based classifiers
1997
In this paper, a set of neighbourhood-based classifiers are jointly used in order to select a more reliable neighbourhood of a given sample and take an appropriate decision about its class membership. The approaches introduced here make use of two concepts: proximity and symmetric placement of the samples.
A novel Bayesian framework for relevance feedback in image content-based retrieval systems
2006
This paper presents a new algorithm for image retrieval in content-based image retrieval systems. The objective of these systems is to get the images which are as similar as possible to a user query from those contained in the global image database without using textual annotations attached to the images. The main problem in obtaining a robust and effective retrieval is the gap between the low level descriptors that can be automatically extracted from the images and the user intention. The algorithm proposed here to address this problem is based on the modeling of user preferences as a probability distribution on the image space. Following a Bayesian methodology, this distribution is the pr…
Network attack detection and classification by the F-transform
2015
We solve the problem of network attack detection and classification. We discuss the way of generation and simulation of an artificial network traffic data. We propose an efficient algorithm for data classification that is based on the F-transform technique. The algorithm successfully passed all tests and moreover, it showed ability to perform classification in an on-line regime.
Analysis of ventricular fibrillation signals using feature selection methods
2012
Feature selection methods in machine learning models are a powerful tool to knowledge extraction. In this work they are used to analyse the intrinsic modifications of cardiac response during ventricular fibrillation due to physical exercise. The data used are two sets of registers from isolated rabbit hearts: control (G1: without physical training), and trained (G2). Four parameters were extracted (dominant frequency, normalized energy, regularity index and number of occurrences). From them, 18 features were extracted. This work analyses the relevance of each feature to classify the records in G1 and G2 using Logistic Regression, Multilayer Perceptron and Extreme Learning Machine. Three fea…
Interactive Image Retrieval Using Smoothed Nearest Neighbor Estimates
2010
Relevance feedback has been adopted by most recent Content Based Image Retrieval systems to reduce the semantic gap that exists between the subjective similarity among images and the similarity measures computed in a given feature space. Distance-based relevance feedback using nearest neighbors has been recently presented as a good tradeoff between simplicity and performance. In this paper, we analyse some shortages of this technique and propose alternatives that help improving the efficiency of the method in terms of the retrieval precision achieved. The resulting method has been evaluated on several repositories which use different feature sets. The results have been compared to those obt…