There are a total of four MDML data sets (i.e., Song-v1, Song-v2, Yeast-v1, Yeast-v2) used in our PRJ'23 paper:

[PRJ'23] B.-B. Jia, M.-L. Zhang. Multi-Dimensional Multi-Label Classification: Towards Encompassing Heterogeneous Label Spaces and Multi-Label Annotations, Pattern Recognition, 2023, in press. 

For more details about these four data sets, please refer to Subsection 4.1.1 in our paper. Running the function "DataStatistics.m" in Matlab can output the characteristics of the data set (e.g., DataStatistics('Song-v1')).

For each dataset (i.e., "DATANAME.mat"),  after loading "DATANAME.mat" in the Matlab environment, you can find five variables named "data_name", "data",  "target",  "numLabel" and "idx_folds" organized in the following way:

"data_name": A string which stores the name of this data set.

"data": An mxd matrix and stores the original input attribute representations, where m is the number of instances and d is the number of features. Here, data(i,:) stores the feature vector of the i-th instance. 

"target": An mxK matrix w.r.t. the labeling information, where K is the number of labels in the whole output space. Here, target(i,:) stores the label vector associated with the i-th instance.

"numLabel": A length-q vector, where the j-th item stores the number of labels in the j-th label space.

"idx_folds": A 10x1 cell w.r.t. the data partition in ten-fold cross validation, where
    -idx_folds{i}.train stores the indexes of training examples in the i-th cross validation,
    -idx_folds{i}.test stores the indexes of testing examples in the i-th cross validation.
    NOTE: These ten-fold cross validation partitions are only given for reference purpose. Please feel free to use your own partitions for experimental evaluation.

