19 datasets found

MEKA is a multi-label/multi-target extension to Weka.

scikit-multilearn is a Python library for performing multi-label classification. The library is compatible with the scikit/scipy ecosystem and uses sparse matrices for all...

MULAN is a Java library for multi-label learning.

The dataset used in the LightDXML paper for extreme multi-label classification.

The dataset used in the LightDXML paper for extreme multi-label classification.

The dataset used in the LightDXML paper for extreme multi-label classification.

The dataset used in the LightDXML paper for extreme multi-label classification.

Multi-label classification deals with the problem where each instance is associated with multiple class labels.

The MuReD dataset was constructed using the ARIA dataset, containing 143 images and three labels to predict, the STARE dataset, with 388 images and 21 conditions, and the...

The dataset used in the LightDXML paper for extreme multi-label classification.

A structured ophthalmic dataset of 5,000 patients with multi-label image-level annotations for eight eye disease categories.

Deep Matrix Approximately Nonlinear Decomposition (DEMAND) employs Adam and an alternative optimization strategy that is well suited to optimize convex/alternative convex...

A dataset of leaf images of six plants (Tomato, Potato, Rice, Corn, Grape, and Apple) with 28 different diseases.

A novel approach for multi-label chest X-ray classification of common thorax diseases.

Multi-label text classification dataset Ren-CECps

Multi-label text classification dataset RCV1-v2, Reuters Corpus Volume I

The DukeMTMC-VID-Attribute dataset is an annotated pedestrian attributes dataset of the DukeMTMC-VID dataset by Chen et al. Four sets of multi-label attributes, such as motion,...

The MARS-Attribute dataset is an annotated pedestrian attributes dataset of the MARS dataset by Chen et al. Five sets of multi-label attributes such as action, pedestrian...

The dataset used in this paper for tuberculosis (TB) prediction using synthetically generated computed tomography (CT) images.