Classification Code
Classification code research focuses on developing and improving algorithms and models to accurately assign data points to predefined categories. Current efforts concentrate on addressing challenges like imbalanced datasets, noisy data, and limited labeled data through techniques such as self-supervised pre-training, robust loss functions, and the application of diverse architectures including convolutional neural networks (CNNs), transformers, and novel approaches like Mamba. These advancements have significant implications across various fields, improving accuracy and efficiency in applications ranging from medical image analysis and bioacoustic monitoring to cybersecurity threat detection and scientific literature organization.
Papers
UNFIS: A Novel Neuro-Fuzzy Inference System with Unstructured Fuzzy Rules for Classification
Armin Salimi-Badr
Differential Privacy has Bounded Impact on Fairness in Classification
Paul Mangold, Michaël Perrot, Aurélien Bellet, Marc Tommasi
Evaluating the Impact of Loss Function Variation in Deep Learning for Classification
Simon Dräger, Jannik Dunkelau
Influence of Utterance and Speaker Characteristics on the Classification of Children with Cleft Lip and Palate
Ilja Baumann, Dominik Wagner, Franziska Braun, Sebastian P. Bayerl, Elmar Nöth, Korbinian Riedhammer, Tobias Bocklet
A Novel Filter Approach for Band Selection and Classification of Hyperspectral Remotely Sensed Images Using Normalized Mutual Information and Support Vector Machines
Hasna Nhaila, Asma Elmaizi, Elkebir Sarhrouni, Ahmed Hammouch
Disentangled and Robust Representation Learning for Bragging Classification in Social Media
Xiang Li, Yucheng Zhou
A novel information gain-based approach for classification and dimensionality reduction of hyperspectral images
Asma Elmaizi, Hasna Nhaila, Elkebir Sarhrouni, Ahmed Hammouch, Chafik Nacir
A new band selection approach based on information theory and support vector machine for hyperspectral images reduction and classification
A. Elmaizi, E. Sarhrouni, A. Hammouch, C. Nacir
A novel filter based on three variables mutual information for dimensionality reduction and classification of hyperspectral images
Asma Elmaizi, Elkebir Sarhrouni, Ahmed hammouch, Chafik Nacir
New wrapper method based on normalized mutual information for dimension reduction and classification of hyperspectral images
Hasna Nhaila, Asma Elmaizi, Elkebir Sarhrouni, Ahmed Hammouch
Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks
Bartosz Grabowski, Przemysław Głomb, Wojciech Masarczyk, Paweł Pławiak, Özal Yıldırım, U Rajendra Acharya, Ru-San Tan
A Novel Approach for Dimensionality Reduction and Classification of Hyperspectral Images based on Normalized Synergy
Asma Elmaizi, Hasna Nhaila, Elkebir Sarhrouni, Ahmed Hammouch, Nacir Chafik
Multi-modal Dynamic Graph Network: Coupling Structural and Functional Connectome for Disease Diagnosis and Classification
Yanwu Yang, Xutao Guo, Zhikai Chang, Chenfei Ye, Yang Xiang, Ting Ma
Band selection and classification of hyperspectral images by minimizing normalized mutual information
E. Sarhrouni, A. Hammouch, D. Aboutajdine
An Algorithm and Heuristic based on Normalized Mutual Information for Dimensionality Reduction and Classification of Hyperspectral images
Elkebir Sarhrouni, Ahmed Hammouch, Driss Aboutajdine