Running Example Datasets - DiRo2C

The two datasets are used to simulate two different black boxes that are supposed to predict different results in certain data areas.

Dataset diro2c_running_example_dataset.csv:

Two-dimensional dataset with the continuous features x1 and x2. It contains 300 instances with the following properties: min = − 293.39, max = 437.35, µ = − 2.92, and σ = 187.69 for feature x1 and min = − 251.96, max = 316.10, µ = − 3.23, and σ = 101.08 for feature x2. The instances of the datasets are classified into two classes “0” and “1”. The instances are generated by the sklearn “make_classification” function with the following parameters: make_classification(n_samples = 300, n_features = 2, n_informative = 1, n_redundant = 0, n_classes = 2, random_state = 2, n_clusters_per_class = 1, class_sep = 1.8, flip_y = 0, scale = 100).

Dataset diro2c_running_example_manipulated_dataset.csv:

This dataset is manipulated on the basis of the first dataset as follows:

 (x1 <= 0 and x2 <= −100 : y = 0), (x1 > 0 and x2 <= −100 : y = 1), (x1 < 150 and x2 > −100 and x2 < 100 : y = 0),
(x1 >= 150 and x2 > −100 and x2 < 100 : y = 1), (x1 = −200 and x2 >= 100: y = 0), (x1 > −200 and x2 >= 100 : y = 1)