Traditional single-label classification is concerned with learning from a set of examples that are associated with a single label l from a set of disjoint labels L. In multi label classification, the examples are associated with a set of labels in L. In the past, multi label classification was mainly motivated by the tasks of text categorization and medical diagnosis. Nowadays, we notice that multi label classification methods are increasingly required by modern applications, such as protein function classification, music categorization and semantic scene classification.
This section shows the multi label data sets avalaible in the repository. Every one defines a supervised classification problem, where each of its examples is composed by some nominal or numerical attributes and a some nominal output attributes (its classes).
Each data file has the following structure:
- @relation: Name of the data set
- @attribute: Description of an attribute (one for each attribute)
- @inputs: List with the names of the input attributes
- @output: List with the names of the output attributes
- @data: Starting tag of the data
The rest of the file contains all the examples belonging to the data set, expressed in comma sepparated values format.
Below you can find all the Multi label data sets available. For each data set, it is shown its name and its number of instances, attributes (Real/Integer/Nominal valued) and labels (number of output variables).
The table allows to download each data set in KEEL format (inside a ZIP file). Additionally, it is possible to obtain the data set already partitioned, by means of a 5-folds cross validation or a 10-folds cross validation procedure.
By clicking in the column headers, you can order the table by names (alphabetically), by the number of examples, attributes, or labels. Clicking again will sort the rows in reverse order.
Collecting Data Sets
If you have some example data sets and you would like to share
them with the rest of the research community by means of this page, please be so
kind as to send your data to the Webmaster Team with the following information:
- People answerable for the data (full name, affiliation, e-mail, web page,
...).
- training and test data sets considered, preferably in ASCII format.
- A brief description of the application.
- References where it is used.
- Results obtained by the methods proposed by the authors or used for comparison.
- Type of experiment developed.
- Any additional useful information.
Collecting Results
If you have applied your methods to some of the problems
presented here we will be glad of showing your results in this page. Please be so kind as to send the following information to Webmaster Team:
- Name of the application considered and type of experiment developed.
- Results obtained by the methods proposed by the authors or used for comparison.
- References where the results are shown.
- Any additional useful information.
Contact Us
If you are interested on being informed of each update made in
this page or you would like to comment on it, please contact with the Webmaster Team.