Ensemble Learning for Multi-type Classification in Heterogeneous Networks

Ensemble Learning for Multi-type Classification in Heterogeneous Networks<br>In this project you can find the following files:<br>a) EnsembleMRSBC.zipThis file contains the systems (Mr-SBC, ST-MrSBC and MT-MrSBC), the datasets used for the experimental evaluation (they are dump databases generated with PostgreSQL 9.5) and, for each dataset, the 10 folds used for the 10-fold cross validation. Moreover, an example of configuration file for the execution of the system is included in the zip file.<br>b) README.txtThis file contains the full instructions for the execution of the system.<br>c) Results_EnsembleMT-MrSBC.xlsThis Excel file contains the results in terms of accuracy obtained on all datasets (according to the selected target types and their target attributes) by the systems: Mr-SBC, ST-MrSBC, MT-MrSBC (Lexicographic ordering), MT-MrSBC (Random ordering), RelIBk (RelWEKA), RelSMO (RelWEKA), HENPC and GNetMine. Results are reported for each fold and for each iteration in the case of our ensemble-based systems ST-MrSBC and MT-MrSBC (both Lexicographic and Random versions).<br>For more details, please refer to the manuscript:F. Serafino, G. Pio, M. Ceci, "Ensemble Learning for Multi-type Classification in Heterogeneous Networks"