Binary classifiers' outputs for ensemble creation

This dataset was created based on the paper 'Andras Hajdu, Gyorgy Terdik, Attila Tiba, and Henrietta Toman:A stochastic approach to handle knapsack problems in the creation of ensembles'.To summarize our experimental setup for UCI binary classification problems, we have considered baseclassifiers perceptron, decision tree, Levenberg-Marquardt feedforward neural network, random neural network,and discriminative restricted Boltzmann machine classifier for the 5 UCI datasets MAGIC Gamma Telescope, HIGGS, EEG EyeState,Musk (Version 2), and Spambase; datasets of large cardinalities were selected to be able to train synthetic variants of base classifiers on different subsets.To check our models for different numbers of possible ensemble members, the respective pool sizes were set to 30 and 100;the necessary number of classifiers has been reached via synthesizing the base classifiers with training them on different subsets of the training part of the given datasets.