Ensemble Learning for Multi-type Classification in Heterogeneous Networks

Ensemble Learning for Multi-type Classification in Heterogeneous NetworksIn this project you can find the following files: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.b) README.txtThis file contains the full instructions for the execution of the system.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).For more details, please refer to the manuscript:F. Serafino, G. Pio, M. Ceci, "Ensemble Learning for Multi-type Classification in Heterogeneous Networks"