DOI: 10.7910/dvn/5owrgb
title: Synthetic datasets for concept drift detection purposes

description: In order to know about the performance of a drift detector by measuring the different detection metrics, we need to know beforehand where a real drift occurs. This is only possible with synthetic datasets. The scikit-multiflow framework allows generating several kinds of synthetic data to simulate the occurrence of drifts.
description: Concretely, we have generated 20 diverse synthetic datasets (10 abrupt and 10 gradual) by using several stream generators and functions, and with a different number of features and noise. They exhibit 4 concepts and 3 drifts at time steps 10000, 20000, and 30000 in the case of abrupt datasets, and at time steps 9500, 20000, and 30500 in the case of gradual ones. In the latter case, the width of the drift is 1000. All of them have 40000 instances in total. Next, the details of the datasets:

– With Sine generator: it is ruled by a sequence of classification functions. Sine A refers to abrupt cases and Sine G to gradual ones. In the case of Sine F1,
the order of the functions is SINE1-reversed SINE1-SINE2-reversed SINE2. For Sine F2 the order is reversed SINE2-SINE2-reversed SINE1-SINE1. There-
fore, Sine stream generator provides 4 different datasets: Sine A F1, Sine A F2, Sine G F1, and Sine G F2. They consists of 2 numerical features, a balanced
binary class, and without noise.

– With Random Tree generator: it is ruled by a sequence of tree random state functions. The parameters max tree depth, min leaf depth, and fraction leaves per level were set to 6, 3, and 0.15 respectively. RT A refers to abrupt cases and RT G to gradual ones. In the case of RT F1, the order of the functions is 8873-9856-7896-2563. For RT F2 the order is reversed 2563-7896-9856-8873. Therefore, Random Tree stream generator provides 4 different datasets: RT A F1, RT A F2, RT G F1, and RT G F2. They consists of 2 numerical features, a balanced binary class, and without noise.

– With Mixed generator: it is ruled by a sequence of classification functions. Mixed A refers to abrupt cases and Mixed G to gradual ones. In the case of
Mixed F1, the order of the functions is 0-1-0-1. For Mixed F2 the order is reversed 1-0-1-0. Therefore, Mixed stream generator provides 4 different datasets:
Mixed A F1, Mixed A F2, Mixed G F1, and Mixed G F2. They consists of 4 numerical features, a balanced binary class, and without noise.

– With Sea generator: it is ruled by a sequence of classification functions. Sea A refers to abrupt cases and Sea G to gradual ones. In the case of Sea F1, the order of the functions is 0-1-2-3. For Sea F2 the order is reversed 3-2-1-0. Therefore, Sea stream generator provides 4 different datasets: Sea A F1, Sea A F2, Sea G F1, and Sea G F2. They consists of 3 numerical features, a balanced binary class, and with the probability that noise will happen in the generation of 0.2 (probability range between 0 and 1).

– With Stagger generator: it is ruled by a sequence of classification functions. Stagger A refers to abrupt cases and Stagger G to gradual ones. In the case
of Stagger F1, the order of the functions is 0-1-2-0. For Stagger F2 the order is reversed 2-1-0-2. Therefore, Stagger stream generator provides 4 different
datasets: Stagger A F1, Stagger A F2, Stagger G F1, and Stagger G F2. They consists of 3 numerical features, a balanced binary class, and without noise.