Dataset of article: Synthetic Datasets Generator for Testing Information Visualization and Machine Learning Techniques and Tools

The dataset has basically 2 dimensions, one for class and one for the features. The variations are specified on top of a default dataset, which has the following characteristics:

• 1.000 entries
• No outliers
• No missing values
• Two dimensions (one relevant feature and one class, no bad features)
• 80\% Class separation
• Two Classes
• No Class Imbalance

Thus, six types of datasets were generated, one for each of the six characteristics in the default dataset. In each type of dataset, the system generated four datasets with slight differences in the associated characteristic. For instance, to vary the effect of the number of outliers, the system created datasets with 10\%, 20\%, 30\%, and 40\% of outliers, without changing the other characteristics. The variations of the characteristics are the following:

• Amount of outliers: [10\%, 20\%, 30\%, 40\%, 50\%]
• Class separation: [100\%, 90\%, 80\%, 70\%, 60\%]
• Amount of missing values: [10\%, 20\%, 30\%, 40\%, 50\%]
• Class imbalance: [50\%-50\%, 40\%-60\%, 30\%-70\%, 20\%-80\%, 10\%-90\%]
• Bad features: [1-1, 1-3, 1-5, 1-7, 1-9]
• Amount of classes: [2, 12, 22, 32, 42]