- author = "Cry Kuranga",
- title = "Genetic programming approach for non-stationary data analytics",
- school = "Faculty of Engineering, Built Environment and Information Technology, University of Pretoria",
- year = "2020",
- address = "South Africa",
- month = "19 " # dec,
- keywords = "genetic algorithms, genetic programming, GPANDA, DE, PSO, nonstationary data, concept drift, nonlinear model, model trees, dynamic data clustering, predictive model, dynamic particle swarm optimization, DynPSO, dynamic genetic programming, DyFor GP, DynGP, dynamic differential evolution, KCDCDynDE",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Thesis_Final_Cry.pdf",
- size = "239 pages",
-
abstract = "Nonstationary data with concept drift occurring is
usually made up of different underlying data generating
processes. Therefore, if the knowledge of the existence
of different segments in the dataset is not taken into
consideration, then the induced predictive model is
distorted by the past existing patterns. Thus, the
challenge posed to a regressor is to select an
appropriate segment that depicts the current underlying
data generating process to be used in a model
induction.
The proposed genetic programming approach for nonstationary data analytics (GPANDA) provides a piecewise nonlinear regression model for nonstationary data. The GPANDA consists of three components: dynamic differential evolution-based clustering algorithm to split the parameter space into subspaces that resemble different data generating processes present in the dataset; the dynamic particle swarm optimization-based model induction technique to induce nonlinear models that describe each generated cluster; and dynamic genetic programming that evolves model trees that define the boundaries of nonlinear models which are expressed as terminal nodes.
If an environmental change is detected in a nonstationary dataset, a dynamic differential evolution-based clustering algorithm clusters the data. For the clusters that change, the dynamic particle swarm optimization-based model induction adapts nonlinear models or induces new models to create an updated genetic programming terminal set and then, the genetic programming evolves a piece wise predictive model that models the dataset.
To evaluate the effectiveness of GPANDA, experimental evaluations were conducted on both artificial and real-world datasets. Two stock market datasets, GDP and CPI were selected to benchmark the performance of the proposed model to the leading studies. The GPANDA outperformed the dynamic genetic programming benchmarks and was competitive to the state-of-art-models.",
-
notes = "datasets: Recurrent, Trend, and
Electricity
https://www.cs.up.ac.za/cs/nicog/NePane.htm
https://www.up.ac.za/media/shared/510/2021/up_part_10_web-upload_new.zp202291.pdf
Supervisor: Nelishia Pillay",
