foodtruck 

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

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 407
Attributes 33
Inputs 21
Labels 12
Labelsets 116
Single labelsets 74
Max frequency 115
Cardinality 2.2899
Density 0.1908
Mean IR 7.0945
SCUMBLE 0.1035
TCS 10.283

Citation

Text

Rivolli, Adriano; Parker, Larissa C; de Carvalho, Andre CPLF (2017). Food Truck Recommendation Using Multi-label Classification. In Portuguese Conference on Artificial Intelligence, 585--596.

BibTeX

@inproceedings{rivolli2017food,
  title={Food Truck Recommendation Using Multi-label Classification},
  author={Rivolli, Adriano and Parker, Larissa C and de Carvalho, Andre CPLF},
  booktitle={Portuguese Conference on Artificial Intelligence},
  pages={585--596},
  year={2017},
  organization={Springer},
  doi={10.1007/978-3-319-65340-2\_48}
}

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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