Joachims, Thorsten (1998). Text Categorization with Suport Vector Machines: Learning with Many Relevant Features. In Proc. 10th European Conference on Machine Learning, 137--142.
ohsumed
mldr.datasets::get.mldr("ohsumed")
Summary
Instances | 13929 |
---|---|
Attributes | 1025 |
Inputs | 1002 |
Labels | 23 |
Labelsets | 1147 |
Single labelsets | 578 |
Max frequency | 1175 |
Cardinality | 1.6631 |
Density | 0.0723 |
Mean IR | 7.8692 |
SCUMBLE | 0.0688 |
TCS | 17.0902 |
Citation
Joachims, Thorsten (1998). Text Categorization with Suport Vector Machines: Learning with Many Relevant Features. In Proc. 10th European Conference on Machine Learning, 137--142.
@inproceedings{,
title="Text Categorization with Suport Vector Machines: Learning with Many Relevant Features",
author="Joachims, Thorsten",
booktitle="Proc. 10th European Conference on Machine Learning",
pages="137--142",
year="1998"
}
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.