Results 81 to 90 of about 881,675 (221)

Multi-label Problem Transformation Methods: a Case Study

CLEI Electronic Journal, 2011
Traditional classification algorithms consider learning problems that contain only one label, i.e., each example is associated with one single nominal target variable characterizing its property.
Everton Alvares Cherman, Maria Carolina Monard, Jean Metz   +2 more
doaj   +1 more source

Multi-Label Feature Selection Based on High-Order Label Correlation Assumption

Entropy, 2020
Multi-label data often involve features with high dimensionality and complicated label correlations, resulting in a great challenge for multi-label learning.
Ping Zhang, Wanfu Gao, Juncheng Hu, Yonghao Li   +3 more
doaj   +1 more source

Empirical study of multi-label classification methods for image annotation and retrieval

, 2010
This paper presents an empirical study of multi-label classification methods, and gives suggestions for multi-label classification that are effective for automatic image annotation applications.
Kouzani, Abbas Z., Nasierding, Gulisong
core   +1 more source

Learning multi-label scene classification [PDF]

Pattern Recognition, 2004
In classic pattern recognition problems, classes are mutually exclusive by definition. Classification errors occur when the classes overlap in the feature space. We examine a different situation, occurring when the classes are, by definition, not mutually exclusive.
Matthew R. Boutell, Jiebo Luo, Xipeng Shen, Christopher M. Brown   +3 more
openaire   +1 more source

Adversarial Extreme Multi-label Classification

, 2018
The goal in extreme multi-label classification is to learn a classifier which can assign a small subset of relevant labels to an instance from an extremely large set of target labels. Datasets in extreme classification exhibit a long tail of labels which have small number of positive training instances.
Babbar, Rohit, Schölkopf, Bernhard
openaire   +4 more sources

Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection

, 2019
Multi-label image classification is a fundamental but challenging task towards general visual understanding. Existing methods found the region-level cues (e.g., features from RoIs) can facilitate multi-label classification.
Bilen Hakan, Chen Shang-Fu, Chen Tianshui, Gong Yunchao, He Kaiming, Hu Hexiang, Lawrence Zitnick C., Li Dong, Li Qiang, Lin Tsung-Yi, Liu Feng, Romero Adriana, Simonyan Karen, Tan Mingkui, Wang Jiang, Wang Zhouxia, Xavier Glorot, Xie Pengtao, Yang Hao, Yeh Chih-Kuan, Zagoruyko Sergey, Zhang Junjie, Zhu Feng, Zhu Yi   +23 more
core   +1 more source

MultiCGCN: Multi-Label Text Classification using GCNs and Heterogeneous Graphs [PDF]

International Journal of Web Research
Multi-label text classification is a critical challenge in natural language processing, where the goal is to assign multiple labels to a given document.
Milad Allahgholi, Hossein Rahmani, Parinaz Soltanzadeh, Aylin Naebzadeh   +3 more
doaj   +1 more source

LP-MLTSVM: Laplacian Multi-Label Twin Support Vector Machine for Semi-Supervised Classification

IEEE Access, 2022
In the machine learning jargon, multi-label classification refers to a task where multiple mutually non-exclusive class labels are assigned to a single instance. Generally, the lack of sufficient labeled training data demanded by a classification task is
Farhad Gharebaghi, Ali Amiri
doaj   +1 more source

A triple-random ensemble classification method for mining multi-label data

, 2010
This paper presents a triple-random ensemble learning method for handling multi-label classification problems. The proposed method integrates and develops the concepts of random subspace, bagging and random k-label sets ensemble learning methods to form ...
Kouzani, Abbas Z., Nasierding, Gulisong, Tsoumakas, Grigorios   +2 more
core   +1 more source

Unsupervised Source Separation for Multi-Label Classification

2022 30th European Signal Processing Conference (EUSIPCO), 2022
This paper exploits blind source separation for the purpose of multi-label classification (MLC). The proposed method addresses the multi-label classification problem in two stages, source separation of data consisting of two or more observed classes followed by a single-label classification.
Mitiche, Imene, Salimy, Alireza, Boreham, Philip, McGrail, Tony, Nesbitt, Alan, Morison, Gordon   +5 more
openaire   +2 more sources