Shebuti Rayana
Supervisors: Dr. Leman Akoglu and Prof. Dr. MD. Saidur Rahman
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Papers by Shebuti Rayana
long proven effective, yet anomaly ensembles have been
barely studied. In this work, we tap into this gap and propose
a new ensemble approach for anomaly mining, with application
to event detection in temporal graphs. Our method aims
to combine results from heterogeneous detectors with varying
outputs, and leverage the evidence from multiple sources
to yield better performance. However, trusting all the results
may deteriorate the overall ensemble accuracy, as some
detectors may fall short and provide inaccurate results depending
on the nature of the data in hand. This suggests that
being selective in which results to combine is vital in building
effective ensembles—hence “less is more”.
In this paper we propose SELECT; an ensemble approach
for anomaly mining that employs novel techniques to
automatically and systematically select the results to assemble
in a fully unsupervised fashion. We apply our method to
event detection in temporal graphs, where SELECT successfully
utilizes five base detectors and seven consensus methods
under a unified ensemble framework. We provide extensive
quantitative evaluation of our approach on five realworld
datasets (four with ground truth), including Enron
email communications, New York Times news corpus, and
World Cup 2014 Twitter news feed. Thanks to its selection
mechanism, SELECT yields superior performance compared
to individual detectors alone, the full ensemble (naively combining
all results), and an existing diversity-based ensemble.
is an important task for its applications in a variety of domains, such as cyber security, online and telecommunications, fault and fraud detection, etc. Despite recent advances
in this area, there does not exist a single winning algorithm
known to work well across different datasets. In fact, designing a single method that is effective on a wide range of
datasets is a challenging task. In this work, we propose an
ensemble approach for event detection and characterization
of dynamic graphs. Our ensemble leverages three different
base detection techniques, the results of which are systematically combined to get a final outcome. What is more,
we characterize the events; by identifying the specific entities, i.e. nodes and edges, that are most responsible for the
detected changes. Our ensemble employs a robust rank aggregation strategy to order both the time points as well as
the entities by the magnitude of their anomalousness, which
as a result yields a superior ranking compared to the base
techniques, thanks to its voting mechanism. Experiments
performed on both simulated (network traffic
flow data with
ground truth) and real data (New York Times news corpus)
show that our proposed ensemble successfully identifies the
important change points in which a given dynamic graph
goes through notable state changes, and reveals the key entities that instantiate these changes.
long proven effective, yet anomaly ensembles have been
barely studied. In this work, we tap into this gap and propose
a new ensemble approach for anomaly mining, with application
to event detection in temporal graphs. Our method aims
to combine results from heterogeneous detectors with varying
outputs, and leverage the evidence from multiple sources
to yield better performance. However, trusting all the results
may deteriorate the overall ensemble accuracy, as some
detectors may fall short and provide inaccurate results depending
on the nature of the data in hand. This suggests that
being selective in which results to combine is vital in building
effective ensembles—hence “less is more”.
In this paper we propose SELECT; an ensemble approach
for anomaly mining that employs novel techniques to
automatically and systematically select the results to assemble
in a fully unsupervised fashion. We apply our method to
event detection in temporal graphs, where SELECT successfully
utilizes five base detectors and seven consensus methods
under a unified ensemble framework. We provide extensive
quantitative evaluation of our approach on five realworld
datasets (four with ground truth), including Enron
email communications, New York Times news corpus, and
World Cup 2014 Twitter news feed. Thanks to its selection
mechanism, SELECT yields superior performance compared
to individual detectors alone, the full ensemble (naively combining
all results), and an existing diversity-based ensemble.
is an important task for its applications in a variety of domains, such as cyber security, online and telecommunications, fault and fraud detection, etc. Despite recent advances
in this area, there does not exist a single winning algorithm
known to work well across different datasets. In fact, designing a single method that is effective on a wide range of
datasets is a challenging task. In this work, we propose an
ensemble approach for event detection and characterization
of dynamic graphs. Our ensemble leverages three different
base detection techniques, the results of which are systematically combined to get a final outcome. What is more,
we characterize the events; by identifying the specific entities, i.e. nodes and edges, that are most responsible for the
detected changes. Our ensemble employs a robust rank aggregation strategy to order both the time points as well as
the entities by the magnitude of their anomalousness, which
as a result yields a superior ranking compared to the base
techniques, thanks to its voting mechanism. Experiments
performed on both simulated (network traffic
flow data with
ground truth) and real data (New York Times news corpus)
show that our proposed ensemble successfully identifies the
important change points in which a given dynamic graph
goes through notable state changes, and reveals the key entities that instantiate these changes.