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Author Eloi Puertas; Sergio Escalera; Oriol Pujol edit   pdf
url  doi
openurl 
  Title Generalized Multi-scale Stacked Sequential Learning for Multi-class Classification Type Journal Article
  Year 2015 Publication Pattern Analysis and Applications Abbreviated Journal (up) PAA  
  Volume 18 Issue 2 Pages 247-261  
  Keywords Stacked sequential learning; Multi-scale; Error-correct output codes (ECOC); Contextual classification  
  Abstract In many classification problems, neighbor data labels have inherent sequential relationships. Sequential learning algorithms take benefit of these relationships in order to improve generalization. In this paper, we revise the multi-scale sequential learning approach (MSSL) for applying it in the multi-class case (MMSSL). We introduce the error-correcting output codesframework in the MSSL classifiers and propose a formulation for calculating confidence maps from the margins of the base classifiers. In addition, we propose a MMSSL compression approach which reduces the number of features in the extended data set without a loss in performance. The proposed methods are tested on several databases, showing significant performance improvement compared to classical approaches.  
  Address  
  Corporate Author Thesis  
  Publisher Springer-Verlag Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1433-7541 ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA;MILAB Approved no  
  Call Number Admin @ si @ PEP2013 Serial 2251  
Permanent link to this record
 

 
Author Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera edit  doi
openurl 
  Title Combining Local and Global Learners in the Pairwise Multiclass Classification Type Journal Article
  Year 2015 Publication Pattern Analysis and Applications Abbreviated Journal (up) PAA  
  Volume 18 Issue 4 Pages 845-860  
  Keywords Multiclass classification; Pairwise approach; One-versus-one  
  Abstract Pairwise classification is a well-known class binarization technique that converts a multiclass problem into a number of two-class problems, one problem for each pair of classes. However, in the pairwise technique, nuisance votes of many irrelevant classifiers may result in a wrong class prediction. To overcome this problem, a simple, but efficient method is proposed and evaluated in this paper. The proposed method is based on excluding some classes and focusing on the most probable classes in the neighborhood space, named Local Crossing Off (LCO). This procedure is performed by employing a modified version of standard K-nearest neighbor and large margin nearest neighbor algorithms. The LCO method takes advantage of nearest neighbor classification algorithm because of its local learning behavior as well as the global behavior of powerful binary classifiers to discriminate between two classes. Combining these two properties in the proposed LCO technique will avoid the weaknesses of each method and will increase the efficiency of the whole classification system. On several benchmark datasets of varying size and difficulty, we found that the LCO approach leads to significant improvements using different base learners. The experimental results show that the proposed technique not only achieves better classification accuracy in comparison to other standard approaches, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes.  
  Address  
  Corporate Author Thesis  
  Publisher Springer London Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1433-7541 ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA;MILAB Approved no  
  Call Number Admin @ si @ BGE2014 Serial 2441  
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Author Zhen Xu; Sergio Escalera; Adrien Pavao; Magali Richard; Wei-Wei Tu; Quanming Yao; Huan Zhao; Isabelle Guyon edit  doi
openurl 
  Title Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform Type Journal Article
  Year 2022 Publication Patterns Abbreviated Journal (up) PATTERNS  
  Volume 3 Issue 7 Pages 100543  
  Keywords Machine learning; data science; benchmark platform; reproducibility; competitions  
  Abstract Obtaining a standardized benchmark of computational methods is a major issue in data-science communities. Dedicated frameworks enabling fair benchmarking in a unified environment are yet to be developed. Here, we introduce Codabench, a meta-benchmark platform that is open sourced and community driven for benchmarking algorithms or software agents versus datasets or tasks. A public instance of Codabench is open to everyone free of charge and allows benchmark organizers to fairly compare submissions under the same setting (software, hardware, data, algorithms), with custom protocols and data formats. Codabench has unique features facilitating easy organization of flexible and reproducible benchmarks, such as the possibility of reusing templates of benchmarks and supplying compute resources on demand. Codabench has been used internally and externally on various applications, receiving more than 130 users and 2,500 submissions. As illustrative use cases, we introduce four diverse benchmarks covering graph machine learning, cancer heterogeneity, clinical diagnosis, and reinforcement learning.  
  Address June 24, 2022  
  Corporate Author Thesis  
  Publisher Science Direct Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA;MILAB Approved no  
  Call Number Admin @ si @ XEP2022 Serial 3764  
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Author Penny Tarling; Mauricio Cantor; Albert Clapes; Sergio Escalera edit  doi
openurl 
  Title Deep learning with self-supervision and uncertainty regularization to count fish in underwater images Type Journal Article
  Year 2022 Publication PloS One Abbreviated Journal (up) Plos  
  Volume 17 Issue 5 Pages e0267759  
  Keywords  
  Abstract Effective conservation actions require effective population monitoring. However, accurately counting animals in the wild to inform conservation decision-making is difficult. Monitoring populations through image sampling has made data collection cheaper, wide-reaching and less intrusive but created a need to process and analyse this data efficiently. Counting animals from such data is challenging, particularly when densely packed in noisy images. Attempting this manually is slow and expensive, while traditional computer vision methods are limited in their generalisability. Deep learning is the state-of-the-art method for many computer vision tasks, but it has yet to be properly explored to count animals. To this end, we employ deep learning, with a density-based regression approach, to count fish in low-resolution sonar images. We introduce a large dataset of sonar videos, deployed to record wild Lebranche mullet schools (Mugil liza), with a subset of 500 labelled images. We utilise abundant unlabelled data in a self-supervised task to improve the supervised counting task. For the first time in this context, by introducing uncertainty quantification, we improve model training and provide an accompanying measure of prediction uncertainty for more informed biological decision-making. Finally, we demonstrate the generalisability of our proposed counting framework through testing it on a recent benchmark dataset of high-resolution annotated underwater images from varying habitats (DeepFish). From experiments on both contrasting datasets, we demonstrate our network outperforms the few other deep learning models implemented for solving this task. By providing an open-source framework along with training data, our study puts forth an efficient deep learning template for crowd counting aquatic animals thereby contributing effective methods to assess natural populations from the ever-increasing visual data.  
  Address  
  Corporate Author Thesis  
  Publisher Public Library of Science Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA;MILAB Approved no  
  Call Number Admin @ si @ TCC2022 Serial 3743  
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Author Oriol Pujol; Sergio Escalera; Petia Radeva edit  openurl
  Title An Incremental Node Embedding Technique for Error Correcting Output Codes Type Journal
  Year 2008 Publication Pattern Recognition Abbreviated Journal (up) PR  
  Volume 41 Issue 2 Pages 713–725  
  Keywords  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ PER2008 Serial 942  
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