GpositivePseAAC 

mldr.datasets::get.mldr("GpositivePseAAC")

Select your download

Full dataset

MULAN/MEKA (.arff) MULAN (.xml) LibSVM KEEL mldr

Partitions: select your desired partitioning strategy, validation and format

Random Stratified Iterative stratified
Hold out MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr
2x5-fold cross validation MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr
10-fold cross validation MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr

Summary

Instances 519
Attributes 444
Inputs 440
Labels 4
Labelsets 7
Single labelsets 2
Max frequency 206
Cardinality 1.0077
Density 0.2519
Mean IR 3.8605
SCUMBLE 0.001
TCS 9.419

Citation

Text

Xu, Jianhua; Liu, Jiali; Yin, Jing; Sun, Chengyu (2016). A multi-label feature extraction algorithm via maximizing feature variance and feature-label dependence simultaneously. In Knowledge-Based Systems, 98(), 172--184.

BibTeX

@article{,
  title={A multi-label feature extraction algorithm via maximizing feature variance and feature-label dependence simultaneously},
  author={Xu, Jianhua and Liu, Jiali and Yin, Jing and Sun, Chengyu},
  journal={Knowledge-Based Systems},
  volume={98},
  pages={172--184},
  year={2016},
  publisher={Elsevier}
}

Concurrence plot

In this concurrence plot, sectors represent labels and links between them depict label co-occurrences. SCUMBLE is a measure designed to assess the concurrence among imbalanced labels.

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