Benchmarking Classifier Performance with Sparse Measurements
نویسنده
چکیده
The presented paper describes a methodology, how to perform benchmarking, when classifier performance measurements are sparse. The described methodology is based on missing value imputation and was demonstrated to work, even when 80% of measurements are missing, for example because of unavailable algorithm implementations or unavailable datasets. The methodology was then applied on 29 relational classifiers & propositional tools and 15 datasets, making it the biggest metaanalysis in relational classification up to date.
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