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Your search keyword '"Bouguelia, Mohamed-Rafik"' showing total 10 results
Start Over Author "Bouguelia, Mohamed-Rafik" Topic machine learning
10 results on '"Bouguelia, Mohamed-Rafik"'

1. Learning-Based Dissimilarity for Clustering Categorical Data.

Author

Rivera Rios, Edgar Jacob, Medina-Pérez, Miguel Angel, Lazo-Cortés, Manuel S., Monroy, Raúl, Nowaczyk, Sławomir, Bouguelia, Mohamed-Rafik, and Fanaee, Hadi

Subjects
MATHEMATICAL category theory, MACHINE learning, KEY performance indicators (Management), METADATA
Abstract

Comparing data objects is at the heart of machine learning. For continuous data, object dissimilarity is usually taken to be object distance; however, for categorical data, there is no universal agreement, for categories can be ordered in several different ways. Most existing category dissimilarity measures characterize the distance among the values an attribute may take using precisely the number of different values the attribute takes (the attribute space) and the frequency at which they occur. These kinds of measures overlook attribute interdependence, which may provide valuable information when capturing per-attribute object dissimilarity. In this paper, we introduce a novel object dissimilarity measure that we call Learning-Based Dissimilarity, for comparing categorical data. Our measure characterizes the distance between two categorical values of a given attribute in terms of how likely it is that such values are confused or not when all the dataset objects with the remaining attributes are used to predict them. To that end, we provide an algorithm that, given a target attribute, first learns a classification model in order to compute a confusion matrix for the attribute. Then, our method transforms the confusion matrix into a per-attribute dissimilarity measure. We have successfully tested our measure against 55 datasets gathered from the University of California, Irvine (UCI) Machine Learning Repository. Our results show that it surpasses, in terms of various performance indicators for data clustering, the most prominent distance relations put forward in the literature. [ABSTRACT FROM AUTHOR]

Published
2021
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2. Multi-Task Representation Learning

Author

Bouguelia, Mohamed-Rafik, Pashami, Sepideh, and Nowaczyk, Sławomir

Subjects
Machine Learning, Feature Learning, Signal Processing, Representation Learning, Signalbehandling, Supervised Learning, Multi-Task Learning
Abstract

The majority of existing machine learning algorithms assume that training examples are already represented with sufficiently good features, in practice ones that are designed manually. This traditional way of preprocessing the data is not only tedious and time consuming, but also not sufficient to capture all the different aspects of the available information. With big data phenomenon, this issue is only going to grow, as the data is rarely collected and analyzed with a specific purpose in mind, and more often re-used for solving different problems. Moreover, the expert knowledge about the problem which allows them to come up with good representations does not necessarily generalize to other tasks. Therefore, much focus has been put on designing methods that can automatically learn features or representations of the data instead of learning from handcrafted features. However, a lot of this work used ad hoc methods and the theoretical understanding in this area is lacking.

Published
2017

3. Meta-learning for efficient unsupervised domain adaptation.

Author

Vettoruzzo, Anna, Bouguelia, Mohamed-Rafik, and Rögnvaldsson, Thorsteinn

Subjects
*DISTRIBUTION (Probability theory), *MACHINE learning, *PROBLEM solving, *PHYSIOLOGICAL adaptation, *PRIOR learning
Abstract

The standard machine learning assumption that training and test data are drawn from the same probability distribution does not hold in many real-world applications due to the inability to reproduce testing conditions at training time. Existing unsupervised domain adaption (UDA) methods address this problem by learning a domain-invariant feature space that performs well on available source domain(s) (labeled training data) and the specific target domain (unlabeled test data). In contrast, instead of simply adapting to domains, this paper aims for an approach that learns to adapt effectively to new unlabeled domains. To do so, we leverage meta-learning to optimize a neural network such that an unlabeled adaptation of its parameters to any domain would yield a good generalization on this latter. The experimental evaluation shows that the proposed approach outperforms standard approaches even when a small amount of unlabeled test data is used for adaptation, demonstrating the benefit of meta-learning prior knowledge from various domains to solve UDA problems. [ABSTRACT FROM AUTHOR]

Published
2024
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4. Comparitive Analysis of Various Transfer Learning Apporaches in Deep CNNs for Image Classification

Author

Tyagi, Arnav, Khandelwal, Rishabh, Shelke, Nitin Arvind, Singh, Jagendra, Rajpal, Dev, Gaware, Ishaan Rajendra, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Santosh, KC, editor, Makkar, Aaisha, editor, Conway, Myra, editor, Singh, Ashutosh K., editor, Vacavant, Antoine, editor, Abou el Kalam, Anas, editor, Bouguelia, Mohamed-Rafik, editor, and Hegadi, Ravindra, editor

Published
2024
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5. FNMD: An Evaluation of Machine Learning and Deep Learning Techniques for Fake News Detection

Author

Daneshvar, Bahareh, Abdi, Asad, Hosseini, Seyyed Mahmoud, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Santosh, KC, editor, Makkar, Aaisha, editor, Conway, Myra, editor, Singh, Ashutosh K., editor, Vacavant, Antoine, editor, Abou el Kalam, Anas, editor, Bouguelia, Mohamed-Rafik, editor, and Hegadi, Ravindra, editor

Published
2024
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6. Sugarcane Bud Detection Using YOLOv5

Author

Sindhe, P. Swathi, Hegadi, Ravindra S., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Santosh, KC, editor, Makkar, Aaisha, editor, Conway, Myra, editor, Singh, Ashutosh K., editor, Vacavant, Antoine, editor, Abou el Kalam, Anas, editor, Bouguelia, Mohamed-Rafik, editor, and Hegadi, Ravindra, editor

Published
2024
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7. Enhancing Android Malware Detection: CFS Based Texture Feature Selection and Ensembled Classifier for Malware App Analysis

Author

Sharma, Tejpal, Rattan, Dhavleesh, Kaur, Parneet, Gupta, Anuj Kumar, Gill, Jagbir Singh, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Santosh, KC, editor, Makkar, Aaisha, editor, Conway, Myra, editor, Singh, Ashutosh K., editor, Vacavant, Antoine, editor, Abou el Kalam, Anas, editor, Bouguelia, Mohamed-Rafik, editor, and Hegadi, Ravindra, editor

Published
2024
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8. A Stack Ensemble Approach for Early Alzheimer Classification Using Machine Learning Algorithms

Author

Kumar, Amit, Sharma, Neha, Chauhan, Rahul, Khare, Akhilendra, Anand, Abhineet, Sharma, Manish, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Santosh, KC, editor, Makkar, Aaisha, editor, Conway, Myra, editor, Singh, Ashutosh K., editor, Vacavant, Antoine, editor, Abou el Kalam, Anas, editor, Bouguelia, Mohamed-Rafik, editor, and Hegadi, Ravindra, editor

Published
2024
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9. CincoCrypto - A Cryptocurrency Price Forecasting Tool for Everyone

Author

de Leyva, Gonzalo Lara, Dwivedi, Ashutosh Dhar, Pedersen, Jens Myrup, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Santosh, KC, editor, Makkar, Aaisha, editor, Conway, Myra, editor, Singh, Ashutosh K., editor, Vacavant, Antoine, editor, Abou el Kalam, Anas, editor, Bouguelia, Mohamed-Rafik, editor, and Hegadi, Ravindra, editor

Published
2024
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10. Breast Cancer Detection Using Optimal Machine Learning Techniques: Uncovering the Most Effective Approach

Author

Joshi, Tanmay, Hegadi, Ravindra, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Santosh, KC, editor, Makkar, Aaisha, editor, Conway, Myra, editor, Singh, Ashutosh K., editor, Vacavant, Antoine, editor, Abou el Kalam, Anas, editor, Bouguelia, Mohamed-Rafik, editor, and Hegadi, Ravindra, editor

Published
2024
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