Generic Multidimensional Data Generator

Disclaimer of Authorship: This repository has been forked from this public project, and describes the design, implementation and usage of the data generator used in [1]. My contributions are as follows:

• Providing the generator's documentation in English, so that more researchers are able to understand and configure it;
• Providing a set of MatLab modules to read the .arff files created by the generator and visualizing the corresponding data typology (binary-classification, 2-dimensional datasets);
• Providing a collection of artificial datasets with different degrees of complexity that researchers may consider in future experiments.

Researchers are welcome to contribute their own extensions and data. This documentation is based on RAPORT-RB-16-14, which has also been translated to REPORT-RB-16-14-EN.

Introduction

The generator of artificial multidimensional and multiclass imbalanced data was designed to prepare datasets for computational experiments that focused on the analysis of the influence of various data difficulty factors on the operation of preprocessing methods and classifiers. The first version of the data generator was developed in the scope of a master's thesis [2] and was limited to two-dimensional and two-class datasets. It also did not include the generation of all considered types of data points. Nevertheless, it allowed to conduct a series of experiments regarding data difficulty factors, as those described in [3,4], among others.

This documentation describes the design, implementation, and usage of the current version of the data generator, which comprises the following features:

• Generation of multidimensional and multiclass datasets (from a practical point of view, 3-4 classes and 3-10 conditional attributes corresponding to individual dimensions are used);

• Definition of classes consisting of one or more regions (meta-balls and meta-cubes) with the ability to create integumental regions that fill the space between regions (usually related to the majority class);

• Possibility of varying the relative distribution of points between regions, and controlling the "density" of points within each region (choosing one of two possible distributions – uniform and normal);

• Generation of minority examples of all types, as thoroughly described in [5]: safe, borderline, rare, and outlier:

  • Safe: Examples lying within individual regions and having a homogeneous neighborhood consisting mainly of points of the same class;
  • Borderline: Coastal examples lying on the periphery/border of individual regions and mixed with points from other classes;
  • Rare: Examples forming small disjuncts (consisting of 2-3 examples) lying far from the regions that make up the class: they represent rare but correct observations;
  • Outlier: Single examples that are far from other examples of the same class, corresponding to "standing-out/thrown" examples.

• Generation of training/test sets, maintaining the location of rare and outlier points (i.e., in a given train/test pair, they appear in similar locations).

The generator has been implemented in Java and enables data to be saved in the .arff format.

How to start the generator

The lastest compiled version of the generator with the necessary libraries can be found in the experiments folder (DataGenSW.jar and lib folder). The generator does not have a specialized user interface - it needs to be called from the command line. To run the project from the command line, go to the experiments folder and type the following:

java -jar DataGenSW.jar -config <configuration_file>

The config-files folder contains several configuration files that can be used to give a quick start to the generator. For example:

java -jar DataGenSW.jar -config config-files/paw3-2d.conf

In case of correct operation, the generator will display a short report with a list of generated files:

[main] INFO pl.poznan.put.cs.idss.generator.Generator - Generating data set(s)
[main] INFO pl.poznan.put.cs.idss.generator.Generator - Pass 1...
[main] INFO pl.poznan.put.cs.idss.generator.Generator - Learning set
[main] INFO pl.poznan.put.cs.idss.generator.ARFFWriter - Saving ARFF file paw3-2d.arff...

Configuration Parameters

The generator's operations are controlled by a set of parameters passed in the configuration file and/or in the command line (parameters can be defined in both, although the values from the command line override the values in the configuration file). This section describes the basic information regarding the generator's configuration parameters. For additional details please see the REPORT-RB-16-14-EN and Generator-Parameters.xls.

As an example, the configuration file of paw3-3d is shown below. This is a binary-class, two-dimensional dataset, composed of 3 regions of the minority class resembling a paw print, and an integumental region of the majority class, filling the remaining space. As established in class.1.exampleTypeRatio = 40:20:30:10, the minority class has 40% safe, 20% borderline, 30% rare, and 10% outlier points.

# paw3-2d
attributes = 2
classes = 2

classRatio = 1:9
minOutlierDistance = 1

defaultRegion.weight = 1
defaultRegion.distribution = U
defaultRegion.borderZone = 1
defaultRegion.noOutlierZone = 1.5
defaultRegion.shape = C
defaultRegion.radius = 2, 1

defaultClass.exampleTypeRatio = 100:0:0:0

class.1.exampleTypeRatio = 40:20:30:10
class.1.regions = 3
class.1.region.1.center = 5,5
class.1.region.1.rotation = 1, 2, 45
class.1.region.2.center = -5,3
class.1.region.2.rotation = 1, 2, -45
class.1.region.3.center = 0,-5

class.2.regions = 1
class.2.region.1.shape = I
class.2.region.1.center = 0,0
class.2.region.1.radius = 10, 10

examples = 1500
fileName = paw3-2d.arff
exampleTypeLabels.classes = 1

A list of parameters and their basic description is as follows:

Parameter	Description
attributes	Number of attributes. attributes = 2
classes	Number of classes. classes = 2
names.classes	List of names of decision classes (comma-separated character strings). names.classes = MIN, MAJ
names.attributes	List of names of the attributes (comma-separated character strings). names.attributes = A1, A2
names.decision	Name of the decision attribute. names.decision = CLASS
classRatio	Proportion of each class. They don't have to sum up to 1 or 100, the generator determines the proportion and converts it to the number of points in each class. classRatio = 1:9
minOutlierDistance	Minimum distance between rare or outlier points belonging to one class, expressed in absolute terms (euclidean distance). The distance is not checked for rare points occuring in one "island" – they may appear very close to each other. minOutlierDistance = 1 Note: For certain configurations (large number of rare and outlier points and long distances between them) the generator may not be able to generate points. If, after a certain number of attempts (currently 10,000), the generation of points fails, the generator stops. To solve this problem, reduce the value of the minOutlierDistance parameter.
defaultRegion.*	Grouping of default settings that can be shared by several regions. They can be overwritten for special regions. defaultRegion.shape = C class.2.region.1.shape = I
defaultRegion.weight	The weight of a region determines what proportion of points from a given class should be included in the region. The sum of weights does not have to sum to 1 or 100, the generator automatically computes the number of points in each region. defaultRegion.weight = 1
defaultRegion.shape	The shape of the region allows values C, R and I. C (circle) means meta-balls, R (rectangle) means meta-cubes and I (integumental) is a special case of a meta-cube, where the empty space is filled (usually with the majority class). Points from type I cannot appear in the core C and R regions. Also, type I regions cannot be rotated and cannot contain borderline, rare or outlier points. In other words, type I regions can only be defined for classes that contain only safe points. defaultRegion.shape = C
defaultRegion.distribution	Distribution of points in a given region, allowing values U (uniform) and N (normal). If nothing is specified, U is used. In the case of normal distribution (N) it is possible to indicate the number of standard deviations in the region by adding a value after the N (e.g., "N, 3"). The number of standard deviations is optional: if it is not explicitly stated, it is assumed to be 1.0. This setting only applies to safe points: borderline points are generated using a uniform distribution (U). Regions of type I also ignore this setting (U distribution is used). defaultRegion.distribution = U
defaultRegion.center	The center of the region, given as a list of real numbers (must be equal to the number of attributes). class.1.region.2.center = -5,3
defaultRegion.border	Method to calculate region dimensions for areas containing safe and borderline points. Possible values are fixed and auto. The fixed value means that the generator uses the values of the radius and borderZone parameters (described below) given by the user and they are not modifiable in any way. On the other hand, the auto value means that the generator determines the dimensions of the areas for the safe and borderline points automatically, based on the order of point types (exampleTypeRatio parameter described below). The radius values are used as baseline values for the region sizes, and the method of their conversion is described in REPORT-RB-16-14-EN. defaultRegion.border = auto
defaultRegion.radius	The size of the region given as radii for the C regions and half of the side lengths for R and I regions. They are given as a list of real numbers, the length of the list must be equal to the number of attributes. If the border parameter is fixed (no conversion), this area is the "core" of the region - this is where safe points are placed. If border = auto, the area contains safe and borderline points. More details on REPORT-RB-16-14-EN. defaultRegion.radius = 10, 10 class.2.region.1.radius = 10, 10
defaultRegion.borderZone	Absolute width of the boundary of the area in which borderline points can be placed (must be a value > 0 if the borderline points are to appear in the class to which the area belongs). There is only one value for all attributes: this value is required if border=fixed, otherwise it is ignored. defaultRegion.borderZone = 1
defaultRegion.noOutlierZone	Absolute width of a zone in which rare and outlier points cannot appear. This zone is located around the zone with borderline points. More information on REPORT-RB-16-14-EN. defaultRegion.noOutlierZone = 1.5
defaultRegion.rotation	The rotation of a region is defined by selecting two dimensions (unique indexes counted from 1) and an angle expressed in degrees. Multiple rotations can be defined for one region - either by one entry containing several triple digits, or by multiple entries. If the rotation is not defined, the regions are not rotated. class.1.region.1.rotation = 1,2,45
defaultClass.*	Grouping of default settings for all decision classes - a similar solution to defaultRegion, which avoids that the same settings are repeatedly defined.
defaultClass.exampleTypeRatio	Classify points in a given class as four real numbers separated by a character ":". Successive numbers indicate points of type safe, borderline, rare and outlier. They do not have to sum to 1 or 100 - the generator determines the proportions and then sets the appropriate number of points. If the class is to contain type I regions, all points belonging to it must be of type safe. If not specified, a 100:0:0:0 configuration (safe points only) is used. defaultClass.exampleTypeRatio = 100:0:0:0
class.i.region.j.*	The definition of the jth region of the ith class overrides and replaces the default settings for the defaultRegion. class.1.region.1.center = 5,5
class.i.regions	Number of regions on the ith class. class.1.regions = 3
class.i.*	Properties of ith class that overrides and replaces the default settings entered for defaultClass. class.2.region.1.shape = I
examples	Total number of points in the entire dataset, i.e., in all decision classes and regions. examples = 1500
exampleTypeLabels.classes	Indication of decision classes for which labels indicating the type of data points are to appear in the generated dataset (labels are created by joining the class name with the name of the point type, e.g., MIN-BORDER). Classes for which different types of examples are to be generate should be indicated by specifying their indexes. If not specified, only the decision class labels will be written in the resulting dataset (without the types of individual points). To generate data for typical experiments, omit this setting. These labels are used for more detailed visualization and verification of generated collections of examples. exampleTypeLabels.classes = 1
fileName	The filename (in .arff format) to which the generated data will be saved. Required if only one file is to be generated (i.e., if the learnTestPairs parameter is not provided). fileName = paw3-2d.arff
learnTestRatio	Distribution of points between training and test sets, expressed as a pair of numbers separated by a character ":". The individual values do not have to sum to 1 or 100 - the generator calculates the proportions and defined the appropriate number of points. If not given, only training sets are generated (i.e., a 100:0 ratio is defined). learnTestRatio = 2:1
learnTestPairs	Number of training/test pairs. If not specified, only the training set is generated. If the learnTestRatio parameter is given and the learnTestPairs is omitted, then the generator will report an error and stop running. learnTestPairs = 5
fileName.learn	Template for the name of the training sets. It should contain the suffix -%d which will be replaced by consecutive indexes from 1 to learnTestPairs. It is mandatory if the learnTestRatio is given, otherwise it can be omitted. fileName.learn = paw3-2d-learn-%d.arff
fileName.test	Template for the name of the test sets. It should contain the suffix -%d which will be replaced by consecutive indexes from 1 to learnTestPairs. It is mandatory if the learnTestRatio is given, otherwise it can be omitted. fileName.test = paw3-2d-learn-%d.arff

Visualization

The .arff files generated can be analysed in several data analysis tool, such as WEKA.
I've added some MatLab modules that can be used to read the .arff files, plot the data, and analyse the data typology of the minority class (safe, border, rare, and outlier examples).

In the current version, it is assumed that class 1 corresponds to the minority class, whereas 2 corresponds to the majority. Only binary-classification is considered and data typology is only analysed for the minority class. Therefore, it is assumed that the class attribute created by the data generator respects one of the following formats:

@attribute CLASS {MIN,MAJ}
@attribute D {1,2}
@attribute LABEL {1-SAFE,1-BORDER,1-RARE,1-OUTLIER,1-DEFAULT,2}
@attribute LABEL {MIN-SAFE,MIN-BORDER,MIN-RARE,MIN-OUTLIER,MIN-DEFAULT,MAJ}

If the data is two- or three-dimensional, the data is plotted in 2D/3D. If the number of attributes/features is higher than 3, then Principal Component Analysis (PCA) is performed, and data is plotted in 2D. If savePlot is activated, the code will store a .png of the respective datastet.

clear all, clc
addpath('arff-to-mat');
addpath('datasets');

savePlot = 0 % do not save plot
% savePlot = 1 % save plot

plotDataTypes('paw3-2d.arff', savePlot);

Below are some visualizations of datasets created using the data generator. Configuration files are available at experiments/config-files and corresponding datasets are in visualization/datasets.

Additional Information

Additional information regarding the data generation (design and implementation details, main packages and classes, scheme of generator operations) is based on RAPORT-RB-16-14, and translated to REPORT-RB-16-14-EN. A list of parameters, their description, possible values, requirements, and example of use is also provided, for ease of use (Generator-Parameters.xls).

References

1. Wojciechowski, S., & Wilk, S. (2017). Difficulty factors and preprocessing in imbalanced data sets: an experimental study on artificial data. Foundations of Computing and Decision Sciences, 42(2), 149-176.
2. K. Kaluzny. Metody dekompozycji w analizie niezrównowazonych liczebnie danych. praca magisterska, 2009. (K. Kaluzny. Methods of decomposition in the analysis of imbalanced data. Master Thesis, 2009.)
3. Napierala, K., Stefanowski, J., & Wilk, S. (2010). Learning from imbalanced data in presence of noisy and borderline examples. In International conference on rough sets and current trends in computing (pp. 158-167). Springer, Berlin, Heidelberg.
4. Stefanowski, J. (2013). Overlapping, rare examples and class decomposition in learning classifiers from imbalanced data. In Emerging paradigms in machine learning (pp. 277-306). Springer, Berlin, Heidelberg.
5. Napierala, K., & Stefanowski, J. (2016). Types of minority class examples and their influence on learning classifiers from imbalanced data. Journal of Intelligent Information Systems, 46(3), 563-597.