Evolving rule-based classifiers with genetic programming on GPUs for drifting data streams
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{CANO:2019:PR,
-
author = "Alberto Cano and Bartosz Krawczyk",
-
title = "Evolving rule-based classifiers with genetic
programming on GPUs for drifting data streams",
-
journal = "Pattern Recognition",
-
volume = "87",
-
pages = "248--268",
-
year = "2019",
-
keywords = "genetic algorithms, genetic programming, Machine
learning, Data streams, Concept drift, Rule-based
classification, GPU, High-performance data mining",
-
ISSN = "0031-3203",
-
DOI = "doi:10.1016/j.patcog.2018.10.024",
-
URL = "http://www.sciencedirect.com/science/article/pii/S0031320318303765",
-
abstract = "Designing efficient algorithms for mining massive
high-speed data streams has become one of the
contemporary challenges for the machine learning
community. Such models must display highest possible
accuracy and ability to swiftly adapt to any kind of
changes, while at the same time being characterized by
low time and memory complexities. However, little
attention has been paid to designing learning systems
that will allow us to gain a better understanding of
incoming data. There are few proposals on how to design
interpretable classifiers for drifting data streams,
yet most of them are characterized by a significant
trade-off between accuracy and interpretability. In
this paper, we show that it is possible to have all of
these desirable properties in one model. We introduce
ERulesD2S: evolving rule-based classifier for drifting
data Streams. By using grammar-guided genetic
programming, we are able to obtain accurate sets of
rules per class that are able to adapt to changes in
the stream without a need for an explicit drift
detector. Additionally, we augment our learning model
with new proposals for rule propagation and data stream
sampling, in order to maintain a balance between
learning and forgetting of concepts. To improve
efficiency of mining massive and non-stationary data,
we implement ERulesD2S parallelized on GPUs. A thorough
experimental study on 30 datasets proves that ERulesD2S
is able to efficiently adapt to any type of concept
drift and outperform state-of-the-art rule-based
classifiers, while using small number of rules. At the
same time ERulesD2S is highly competitive to other
single and ensemble learners in terms of accuracy and
computational complexity, while offering fully
interpretable classification rules. Additionally, we
show that ERulesD2S can scale-up efficiently to
high-dimensional data streams, while offering very fast
update and classification times. Finally, we present
the learning capabilities of ERulesD2S for sparsely
labeled data streams",
- }
Genetic Programming entries for
Alberto Cano Rojas
Bartosz Krawczyk
Citations