Multi-Label Datasets with Missing Values
Authors/Creators
- 1. UEMA
- 2. Fuji Electric Co. Ltd.
- 3. UFOPA
Description
Consisting of six multi-label datasets from the UCI Machine Learning repository.
Each dataset contains missing values which have been artificially added at the following rates: 5, 10, 15, 20, 25, and
30%. The “amputation” was performed using the “Missing Completely at Random” mechanism.
File names are represented as follows:
amp_DB_MR.arff
where:
DB = original dataset;
MR = missing rate.
Jacob Junior, A. F. L., do Carmo, F. A., de Santana, A. L., Santana, E. E. C., & Lobato, F. M. F. (2024). EvoImp: Multiple Imputation of Multi-label Classification data with a genetic algorithm. Plos one, 19(1), e0297147. https://doi.org/10.1371/journal.pone.0297147
Files
Files
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