Search results for "UML"
showing 10 items of 407 documents
A genetic integrated fuzzy classifier
2005
This paper introduces a new classifier, that is based on fuzzy-integration schemes controlled by a genetic optimisation procedure. Two different types of integration are proposed here, and are validated by experiments on real data sets of biological cells. The performance of our classifier is tested against a feed-forward neural network and a Support Vector Machine. Results show the good performance and robustness of the integrated classifier strategies.
Combining one class fuzzy KNN’s
2007
This paper introduces a parallel combination of N > 2 one class fuzzy KNN (FKNN) classifiers. The classifier combination consists of a new optimization procedure based on a genetic algorithm applied to FKNN’s, that differ in the kind of similarity used. We tested the integration techniques in the case of N = 5 similarities that have been recently introduced to face with categorical data sets. The assessment of the method has been carried out on two public data set, the Masquerading User Data (www.schonlau.net) and the badges database on the UCI Machine Learning Repository (http://www.ics.uci.edu/~mlearn/). Preliminary results show the better performance obtained by the fuzzy integration …
2D motif basis applied to the classification of digital images
2016
The classification of raw data often involves the problem of selecting the appropriate set of features to represent the input data. Different types of features can be extracted from the input dataset, but only some of them are actually relevant for the classification process. Since relevant features are often unknown in real-world problems, many candidate features are usually introduced. This degrades both the speed and the predictive accuracy of the classifier due to the presence of redundancy in the set of candidate features. Recently, a special class of bidimensional motifs, i.e. 2D motif basis has been introduced in the literature. 2D motif basis showed to be powerful in capturing the r…
A Machine Learning Approach for Fall Detection Based on the Instantaneous Doppler Frequency
2019
Modern societies are facing an ageing problem that is accompanied by increasing healthcare costs. A major share of this ever-increasing cost is due to fall-related injuries, which urges the development of fall detection systems. In this context, this paper paves the way for the development of radio-frequency-based fall detection systems, which do not require the user to wear any device and can detect falls without compromising the user's privacy. For the design of such systems, we present an activity simulator that generates the complex path gain of indoor channels in the presence of one person performing three different activities: slow fall, fast fall, and walking. We have developed a mac…
Online assessment of strategic reading literacy skills
2015
This study investigates the possibility of assessing strategic reading literacy skills with computers. The critical value of this assessment is the recording of online indices of the reader's behavior that can be interpreted in terms of strategies. The study uses materials of a standardized paper-and-pencil reading literacy test called CompLEC (Llorens et?al., 2011) and a technology called Read&Answer (Vidal-Abarca et?al., 2011) that presents texts and questions with a masking procedure that allows the recording of reading time and readers' actions to develop a computer-based version called e-CompLEC. We found that reliability and validity of the two versions are largely equivalent, and tha…
HyperLabelMe : A Web Platform for Benchmarking Remote-Sensing Image Classifiers
2017
HyperLabelMe is a web platform that allows the automatic benchmarking of remote-sensing image classifiers. To demonstrate this platform's attributes, we collected and harmonized a large data set of labeled multispectral and hyperspectral images with different numbers of classes, dimensionality, noise sources, and levels. The registered user can download training data pairs (spectra and land cover/use labels) and submit the predictions for unseen testing spectra. The system then evaluates the accuracy and robustness of the classifier, and it reports different scores as well as a ranked list of the best methods and users. The system is modular, scalable, and ever-growing in data sets and clas…
Klar – klärer – am klärsten? Umlaut comparison as a doubtful case in contemporary German
2017
Abstract The present paper addresses doubtful cases concerning the use of umlaut in the adjectival comparison of contemporary German: bang ‘anxious’ - banger/bänger - am bangsten/ bängsten. It aims to shed light on the concrete distribution of this variation, i.e. the preference for one of the variants. Corpus-based analyses will show that the adjectives under discussion are not equally affected by umlaut variation: some are (surprisingly) stable (e.g., gesund ‘healthy’), whereas many others have a clear preference (i.e. > 70%) for non-umlauting forms (e.g., blass ‘pale’, nass ‘wet’). Interestingly, a few of the supposedly stable cases appear to have at least some non-umlauting forms (e.…
Trends in excellent self-rated health among adolescents: A comparative Nordic study
2019
Abstract Background: Excellent self-rated health (SRH) can be seen as an important component of positive health among adolescents. The aim of this paper is to examine time trends of excellent health among adolescents in five Nordic countries (Denmark, Finland, Iceland, Norway and Sweden) between 2002 and 2014, including differences between countries, gender and age. Methods: Nordic data from the Health Behaviour in School-aged Children (HBSC) survey (including 11-, 13- and 15-year-olds) from 2002 (n = 19,009), 2006 (n = 29,656), 2010 (n = 33,232) and 2014 (n = 31,540) were analysed by design-adjusted binomial logistic regression models. Results: The trend analysis of excellent SRH for Nordi…
Incremental Gaussian Discriminant Analysis based on Graybill and Deal weighted combination of estimators for brain tumour diagnosis
2011
In the last decade, machine learning (ML) techniques have been used for developing classifiers for automatic brain tumour diagnosis. However, the development of these ML models rely on a unique training set and learning stops once this set has been processed. Training these classifiers requires a representative amount of data, but the gathering, preprocess, and validation of samples is expensive and time-consuming. Therefore, for a classical, non-incremental approach to ML, it is necessary to wait long enough to collect all the required data. In contrast, an incremental learning approach may allow us to build an initial classifier with a smaller number of samples and update it incrementally…
Domain separation for efficient adaptive active learning
2011
This paper proposes a procedure aimed at efficiently adapting a classifier trained on a source image to a similar target image. The adaptation is carried out through active queries in the target domain following a strategy particularly designed for the case where class distributions have shifted between the two images. We first suggest a pre-selection of candidate pixels issued from the target image by keeping only those samples appearing to be lying in a region of the input space not yet covered by the existing ground truth (source domain pixels). Then, exploiting a classifier integrating instance weights, active queries are performed on the target image. As the inclusion to the training s…