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Author Fernando Vilariño; Ludmila I. Kuncheva; Petia Radeva edit  doi
openurl 
  Title ROC curves and video analysis optimization in intestinal capsule endoscopy Type Journal Article
  Year 2006 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume (down) 27 Issue 8 Pages 875–881  
  Keywords ROC curves; Classification; Classifiers ensemble; Detection of intestinal contractions; Imbalanced classes; Wireless capsule endoscopy  
  Abstract Wireless capsule endoscopy involves inspection of hours of video material by a highly qualified professional. Time episodes corresponding to intestinal contractions, which are of interest to the physician constitute about 1% of the video. The problem is to label automatically time episodes containing contractions so that only a fraction of the video needs inspection. As the classes of contraction and non-contraction images in the video are largely imbalanced, ROC curves are used to optimize the trade-off between false positive and false negative rates. Classifier ensemble methods and simple classifiers were examined. Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles. By using ROC curves with the bagging ensemble method the inspection time can be drastically reduced at the expense of a small fraction of missed contractions.  
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  ISSN ISBN Medium  
  Area 800 Expedition Conference  
  Notes MILAB;MV;SIAI Approved no  
  Call Number BCNPCL @ bcnpcl @ VKR2006; IAM @ iam @ VKR2006 Serial 647  
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Author Santiago Segui; Laura Igual; Jordi Vitria edit   pdf
doi  openurl
  Title Bagged One Class Classifiers in the Presence of Outliers Type Journal Article
  Year 2013 Publication International Journal of Pattern Recognition and Artificial Intelligence Abbreviated Journal IJPRAI  
  Volume (down) 27 Issue 5 Pages 1350014-1350035  
  Keywords One-class Classifier; Ensemble Methods; Bagging and Outliers  
  Abstract The problem of training classifiers only with target data arises in many applications where non-target data are too costly, difficult to obtain, or not available at all. Several one-class classification methods have been presented to solve this problem, but most of the methods are highly sensitive to the presence of outliers in the target class. Ensemble methods have therefore been proposed as a powerful way to improve the classification performance of binary/multi-class learning algorithms by introducing diversity into classifiers.
However, their application to one-class classification has been rather limited. In
this paper, we present a new ensemble method based on a non-parametric weighted bagging strategy for one-class classification, to improve accuracy in the presence of outliers. While the standard bagging strategy assumes a uniform data distribution, the method we propose here estimates a probability density based on a forest structure of the data. This assumption allows the estimation of data distribution from the computation of simple univariate and bivariate kernel densities. Experiments using original and noisy versions of 20 different datasets show that bagging ensemble methods applied to different one-class classifiers outperform base one-class classification methods. Moreover, we show that, in noisy versions of the datasets, the non-parametric weighted bagging strategy we propose outperforms the classical bagging strategy in a statistically significant way.		  
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  Area Expedition Conference  
  Notes OR; 600.046;MV Approved no  
  Call Number Admin @ si @ SIV2013 Serial 2256  
Permanent link to this record
 
 
Author Fadi Dornaika; Abdelmalik Moujahid; Bogdan Raducanu edit   pdf
url  doi
openurl 
  Title Facial expression recognition using tracked facial actions: Classifier performance analysis Type Journal Article
  Year 2013 Publication Engineering Applications of Artificial Intelligence Abbreviated Journal EAAI  
  Volume (down) 26 Issue 1 Pages 467-477  
  Keywords Visual face tracking; 3D deformable models; Facial actions; Dynamic facial expression recognition; Human–computer interaction  
  Abstract In this paper, we address the analysis and recognition of facial expressions in continuous videos. More precisely, we study classifiers performance that exploit head pose independent temporal facial action parameters. These are provided by an appearance-based 3D face tracker that simultaneously provides the 3D head pose and facial actions. The use of such tracker makes the recognition pose- and texture-independent. Two different schemes are studied. The first scheme adopts a dynamic time warping technique for recognizing expressions where training data are given by temporal signatures associated with different universal facial expressions. The second scheme models temporal signatures associated with facial actions with fixed length feature vectors (observations), and uses some machine learning algorithms in order to recognize the displayed expression. Experiments quantified the performance of different schemes. These were carried out on CMU video sequences and home-made video sequences. The results show that the use of dimension reduction techniques on the extracted time series can improve the classification performance. Moreover, these experiments show that the best recognition rate can be above 90%.  
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  Publisher Elsevier Place of Publication Editor  
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  Area Expedition Conference  
  Notes OR; 600.046;MV Approved no  
  Call Number Admin @ si @ DMR2013 Serial 2185  
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Author M. Bressan; Jordi Vitria edit  doi
openurl 
  Title Nonparametric Discriminant Analysis and Nearest Neighbor Classification Type Journal Article
  Year 2003 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume (down) 24 Issue 15 Pages 2743–2749  
  Keywords  
  Abstract IF: 0.809  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ BrV2003b Serial 367  
Permanent link to this record
 
 
Author David Guillamet; Jordi Vitria edit  doi
openurl 
  Title Evaluation of distance metrics for recognition based on non-negative matrix factorization Type Journal Article
  Year 2003 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume (down) 24 Issue 9-10 Pages 1599 –1605  
  Keywords  
  Abstract IF: 0.809  
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  Area Expedition Conference  
  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ GuV2003b Serial 380  
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