title: Online semi-supervised learning in non-stationary environments
Creators: name: Idrees, Mobin M.
Publisher: name: University of Reading
Year: 2024

description: Existing Data Stream Mining (DSM) algorithms assume the availability of labelled and 
balanced data, immediately or after some delay, to extract worthwhile knowledge from the 
continuous and rapid data streams. However, in many real-world applications such as 
Robotics, Weather Monitoring, Fraud Detection Systems, Cyber Security, and Computer 
Network Traffic Flow, an enormous amount of high-speed data is generated by Internet of 
Things sensors and real-time data on the Internet. Manual labelling of these data streams 
is not practical due to time consumption and the need for domain expertise. Another 
challenge is learning under Non-Stationary Environments (NSEs), which occurs due to 
changes in the data distributions in a set of input variables and/or class labels. The problem 
of Extreme Verification Latency (EVL) under NSEs is referred to as Initially Labelled Non-Stationary Environment (ILNSE). This is a challenging task because the learning algorithms 
have no access to the true class labels directly when the concept evolves. Several approaches 
exist that deal with NSE and EVL in isolation. However, few algorithms address both issues 
simultaneously. This research directly responds to ILNSE’s challenge in proposing two 
novel algorithms “Predictor for Streaming Data with Scarce Labels” (PSDSL) and 
Heterogeneous Dynamic Weighted Majority (HDWM) classifier. PSDSL is an Online Semi-Supervised Learning (OSSL) method for real-time DSM and is closely related to label 
scarcity issues in online machine learning. 
The key capabilities of PSDSL include learning from a small amount of labelled data in an 
incremental or online manner and being available to predict at any time. To achieve this, 
PSDSL utilises both labelled and unlabelled data to train the prediction models, meaning it 
continuously learns from incoming data and updates the model as new labelled or 
unlabelled data becomes available over time. Furthermore, it can predict under NSE
conditions under the scarcity of class labels. PSDSL is built on top of the HDWM classifier, 
which preserves the diversity of the classifiers. PSDSL and HDWM can intelligently switch 
and adapt to the conditions. The PSDSL adapts to learning states between self-learning, 
micro-clustering and CGC, whichever approach is beneficial, based on the characteristics of 
the data stream. HDWM makes use of “seed” learners of different types in an ensemble to 
maintain its diversity. The ensembles are simply the combination of predictive models 
grouped to improve the predictive performance of a single classifier.
PSDSL is empirically evaluated against COMPOSE, LEVELIW, SCARGC and MClassification 
on benchmarks, NSE datasets as well as Massive Online Analysis (MOA) data streams and real-world datasets. The results showed that PSDSL performed significantly better than 
existing approaches on most real-time data streams including randomised data instances.
PSDSL performed significantly better than ‘Static’ i.e. the classifier is not updated after it is 
trained with the first examples in the data streams. When applied to MOA-generated data 
streams, PSDSL ranked highest (1.5) and thus performed significantly better than SCARGC, 
while SCARGC performed the same as the Static. PSDSL achieved better average prediction 
accuracies in a short time than SCARGC.
The HDWM algorithm is evaluated on artificial and real-world data streams against existing 
well-known approaches such as the heterogeneous WMA and the homogeneous Dynamic 
DWM algorithm. The results showed that HDWM performed significantly better than WMA 
and DWM. Also, when recurring concept drifts were present, the predictive performance of 
HDWM showed an improvement over DWM. In both drift and real-world streams, 
significance tests and post hoc comparisons found significant differences between 
algorithms, HDWM performed significantly better than DWM and WMA when applied to 
MOA data streams and 4 real-world datasets Electric, Spam, Sensor and Forest cover. The 
seeding mechanism and dynamic inclusion of new base learners in the HDWM algorithms 
benefit from the use of both forgetting and retaining the models. The algorithm also 
provides the independence of selecting the optimal base classifier in its ensemble depending 
on the problem.
A new approach, Envelope-Clustering is introduced to resolve the cluster overlap conflicts 
during the cluster labelling process. In this process, PSDSL transforms the centroids’ 
information of micro-clusters into micro-instances and generates new clusters called 
Envelopes. The nearest envelope clusters assist the conflicted micro-clusters and 
successfully guide the cluster labelling process after the concept drifts in the absence of true 
class labels. PSDSL has been evaluated on real-world problem ‘keystroke dynamics’, and 
the results show that PSDSL achieved higher prediction accuracy (85.3%) and SCARGC 
(81.6%), while the Static (49.0%) significantly degrades the performance due to changes in 
the users typing pattern. Furthermore, the predictive accuracies of SCARGC are found 
highly fluctuated between (41.1% to 81.6%) based on different values of parameter ‘k’ 
(number of clusters), while PSDSL automatically determine the best values for this
parameter.