2 research outputs found
Evolving Large-Scale Data Stream Analytics based on Scalable PANFIS
Many distributed machine learning frameworks have recently been built to
speed up the large-scale data learning process. However, most distributed
machine learning used in these frameworks still uses an offline algorithm model
which cannot cope with the data stream problems. In fact, large-scale data are
mostly generated by the non-stationary data stream where its pattern evolves
over time. To address this problem, we propose a novel Evolving Large-scale
Data Stream Analytics framework based on a Scalable Parsimonious Network based
on Fuzzy Inference System (Scalable PANFIS), where the PANFIS evolving
algorithm is distributed over the worker nodes in the cloud to learn
large-scale data stream. Scalable PANFIS framework incorporates the active
learning (AL) strategy and two model fusion methods. The AL accelerates the
distributed learning process to generate an initial evolving large-scale data
stream model (initial model), whereas the two model fusion methods aggregate an
initial model to generate the final model. The final model represents the
update of current large-scale data knowledge which can be used to infer future
data. Extensive experiments on this framework are validated by measuring the
accuracy and running time of four combinations of Scalable PANFIS and other
Spark-based built in algorithms. The results indicate that Scalable PANFIS with
AL improves the training time to be almost two times faster than Scalable
PANFIS without AL. The results also show both rule merging and the voting
mechanisms yield similar accuracy in general among Scalable PANFIS algorithms
and they are generally better than Spark-based algorithms. In terms of running
time, the Scalable PANFIS training time outperforms all Spark-based algorithms
when classifying numerous benchmark datasets.Comment: 20 pages, 5 figure
Deep Stacked Stochastic Configuration Networks for Lifelong Learning of Non-Stationary Data Streams
The concept of SCN offers a fast framework with universal approximation
guarantee for lifelong learning of non-stationary data streams. Its adaptive
scope selection property enables for proper random generation of hidden unit
parameters advancing conventional randomized approaches constrained with a
fixed scope of random parameters. This paper proposes deep stacked stochastic
configuration network (DSSCN) for continual learning of non-stationary data
streams which contributes two major aspects: 1) DSSCN features a
self-constructing methodology of deep stacked network structure where hidden
unit and hidden layer are extracted automatically from continuously generated
data streams; 2) the concept of SCN is developed to randomly assign inverse
covariance matrix of multivariate Gaussian function in the hidden node addition
step bypassing its computationally prohibitive tuning phase. Numerical
evaluation and comparison with prominent data stream algorithms under two
procedures: periodic hold-out and prequential test-then-train processes
demonstrate the advantage of proposed methodology.Comment: This paper has been published in Information Science