2,527 research outputs found
Rebalancing Learning on Evolving Data Streams
Nowadays, every device connected to the Internet generates an ever-growing
stream of data (formally, unbounded). Machine Learning on unbounded data
streams is a grand challenge due to its resource constraints. In fact, standard
machine learning techniques are not able to deal with data whose statistics is
subject to gradual or sudden changes without any warning. Massive Online
Analysis (MOA) is the collective name, as well as a software library, for new
learners that are able to manage data streams. In this paper, we present a
research study on streaming rebalancing. Indeed, data streams can be imbalanced
as static data, but there is not a method to rebalance them incrementally, one
element at a time. For this reason we propose a new streaming approach able to
rebalance data streams online. Our new methodology is evaluated against some
synthetically generated datasets using prequential evaluation in order to
demonstrate that it outperforms the existing approaches
Preventing Discriminatory Decision-making in Evolving Data Streams
Bias in machine learning has rightly received significant attention over the
last decade. However, most fair machine learning (fair-ML) work to address bias
in decision-making systems has focused solely on the offline setting. Despite
the wide prevalence of online systems in the real world, work on identifying
and correcting bias in the online setting is severely lacking. The unique
challenges of the online environment make addressing bias more difficult than
in the offline setting. First, Streaming Machine Learning (SML) algorithms must
deal with the constantly evolving real-time data stream. Second, they need to
adapt to changing data distributions (concept drift) to make accurate
predictions on new incoming data. Adding fairness constraints to this already
complicated task is not straightforward. In this work, we focus on the
challenges of achieving fairness in biased data streams while accounting for
the presence of concept drift, accessing one sample at a time. We present Fair
Sampling over Stream (), a novel fair rebalancing approach capable of
being integrated with SML classification algorithms. Furthermore, we devise the
first unified performance-fairness metric, Fairness Bonded Utility (FBU), to
evaluate and compare the trade-off between performance and fairness of
different bias mitigation methods efficiently. FBU simplifies the comparison of
fairness-performance trade-offs of multiple techniques through one unified and
intuitive evaluation, allowing model designers to easily choose a technique.
Overall, extensive evaluations show our measures surpass those of other fair
online techniques previously reported in the literature
PWIDB: A framework for learning to classify imbalanced data streams with incremental data re-balancing technique
The performance of classification algorithms with highly imbalanced streaming data depends upon efficient balancing strategy. Some techniques of balancing strategy have been applied using static batch data to resolve the class imbalance problem, which is difficult if applied for massive data streams. In this paper, a new Piece-Wise Incremental Data re-Balancing (PWIDB) framework is proposed. The PWIDB framework combines automated balancing techniques using Racing Algorithm (RA) and incremental rebalancing technique. RA is an active learning approach capable of classifying imbalanced data and can provide a way to select an appropriate re-balancing technique with imbalanced data. In this paper, we have extended the capability of RA for handling imbalanced data streams in the proposed PWIDB framework. The PWIDB accumulates previous knowledge with increments of re-balanced data and captures the concept of the imbalanced instances. The PWIDB is an incremental streaming batch framework, which is suitable for learning with streaming imbalanced data. We compared the performance of PWIDB with a well-known FLORA technique. Experimental results show that the PWIDB framework exhibits an improved and stable performance compared to FLORA and accumulative re-balancing techniques
Fault diagnosis for IP-based network with real-time conditions
BACKGROUND:
Fault diagnosis techniques have been based on many paradigms, which derive from diverse areas
and have different purposes: obtaining a representation model of the network for fault localization,
selecting optimal probe sets for monitoring network devices, reducing fault detection time, and
detecting faulty components in the network. Although there are several solutions for diagnosing
network faults, there are still challenges to be faced: a fault diagnosis solution needs to always be
available and able enough to process data timely, because stale results inhibit the quality and speed
of informed decision-making. Also, there is no non-invasive technique to continuously diagnose the
network symptoms without leaving the system vulnerable to any failures, nor a resilient technique
to the network's dynamic changes, which can cause new failures with different symptoms.
AIMS:
This thesis aims to propose a model for the continuous and timely diagnosis of IP-based networks
faults, independent of the network structure, and based on data analytics techniques.
METHOD(S):
This research's point of departure was the hypothesis of a fault propagation phenomenon that
allows the observation of failure symptoms at a higher network level than the fault origin. Thus, for
the model's construction, monitoring data was collected from an extensive campus network in
which impact link failures were induced at different instants of time and with different duration.
These data correspond to widely used parameters in the actual management of a network. The
collected data allowed us to understand the faults' behavior and how they are manifested at a
peripheral level.
Based on this understanding and a data analytics process, the first three modules of our model,
named PALADIN, were proposed (Identify, Collection and Structuring), which define the data
collection peripherally and the necessary data pre-processing to obtain the description of the
network's state at a given moment. These modules give the model the ability to structure the data
considering the delays of the multiple responses that the network delivers to a single monitoring
probe and the multiple network interfaces that a peripheral device may have.
Thus, a structured data stream is obtained, and it is ready to be analyzed. For this analysis, it was
necessary to implement an incremental learning framework that respects networks' dynamic
nature. It comprises three elements, an incremental learning algorithm, a data rebalancing strategy,
and a concept drift detector. This framework is the fourth module of the PALADIN model named
Diagnosis.
In order to evaluate the PALADIN model, the Diagnosis module was implemented with 25 different
incremental algorithms, ADWIN as concept-drift detector and SMOTE (adapted to streaming scenario) as the rebalancing strategy. On the other hand, a dataset was built through the first
modules of the PALADIN model (SOFI dataset), which means that these data are the incoming data
stream of the Diagnosis module used to evaluate its performance.
The PALADIN Diagnosis module performs an online classification of network failures, so it is a
learning model that must be evaluated in a stream context. Prequential evaluation is the most used
method to perform this task, so we adopt this process to evaluate the model's performance over
time through several stream evaluation metrics.
RESULTS:
This research first evidences the phenomenon of impact fault propagation, making it possible to
detect fault symptoms at a monitored network's peripheral level. It translates into non-invasive
monitoring of the network. Second, the PALADIN model is the major contribution in the fault
detection context because it covers two aspects. An online learning model to continuously process
the network symptoms and detect internal failures. Moreover, the concept-drift detection and
rebalance data stream components which make resilience to dynamic network changes possible.
Third, it is well known that the amount of available real-world datasets for imbalanced stream
classification context is still too small. That number is further reduced for the networking context.
The SOFI dataset obtained with the first modules of the PALADIN model contributes to that number
and encourages works related to unbalanced data streams and those related to network fault
diagnosis.
CONCLUSIONS:
The proposed model contains the necessary elements for the continuous and timely diagnosis of IPbased
network faults; it introduces the idea of periodical monitorization of peripheral network
elements and uses data analytics techniques to process it. Based on the analysis, processing, and
classification of peripherally collected data, it can be concluded that PALADIN achieves the
objective. The results indicate that the peripheral monitorization allows diagnosing faults in the
internal network; besides, the diagnosis process needs an incremental learning process, conceptdrift
detection elements, and rebalancing strategy.
The results of the experiments showed that PALADIN makes it possible to learn from the network
manifestations and diagnose internal network failures. The latter was verified with 25 different
incremental algorithms, ADWIN as concept-drift detector and SMOTE (adapted to streaming
scenario) as the rebalancing strategy.
This research clearly illustrates that it is unnecessary to monitor all the internal network elements
to detect a network's failures; instead, it is enough to choose the peripheral elements to be
monitored. Furthermore, with proper processing of the collected status and traffic descriptors, it is
possible to learn from the arriving data using incremental learning in cooperation with data
rebalancing and concept drift approaches. This proposal continuously diagnoses the network
symptoms without leaving the system vulnerable to failures while being resilient to the network's
dynamic changes.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José Manuel Molina López.- Secretario: Juan Carlos Dueñas López.- Vocal: Juan Manuel Corchado Rodrígue
Resource optimization of edge servers dealing with priority-based workloads by utilizing service level objective-aware virtual rebalancing
IoT enables profitable communication between sensor/actuator devices and the cloud. Slow network causing Edge data to lack Cloud analytics hinders real-time analytics adoption. VRebalance solves priority-based workload performance for stream processing at the Edge. BO is used in VRebalance to prioritize workloads and find optimal resource configurations for efficient resource management. Apache Storm platform was used with RIoTBench IoT benchmark tool for real-time stream processing. Tools were used to evaluate VRebalance. Study shows VRebalance is more effective than traditional methods, meeting SLO targets despite system changes. VRebalance decreased SLO violation rates by almost 30% for static priority-based workloads and 52.2% for dynamic priority-based workloads compared to hill climbing algorithm. Using VRebalance decreased SLO violations by 66.1% compared to Apache Storm\u27s default allocation
A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework
Class imbalance poses new challenges when it comes to classifying data
streams. Many algorithms recently proposed in the literature tackle this
problem using a variety of data-level, algorithm-level, and ensemble
approaches. However, there is a lack of standardized and agreed-upon procedures
on how to evaluate these algorithms. This work presents a taxonomy of
algorithms for imbalanced data streams and proposes a standardized, exhaustive,
and informative experimental testbed to evaluate algorithms in a collection of
diverse and challenging imbalanced data stream scenarios. The experimental
study evaluates 24 state-of-the-art data streams algorithms on 515 imbalanced
data streams that combine static and dynamic class imbalance ratios,
instance-level difficulties, concept drift, real-world and semi-synthetic
datasets in binary and multi-class scenarios. This leads to the largest
experimental study conducted so far in the data stream mining domain. We
discuss the advantages and disadvantages of state-of-the-art classifiers in
each of these scenarios and we provide general recommendations to end-users for
selecting the best algorithms for imbalanced data streams. Additionally, we
formulate open challenges and future directions for this domain. Our
experimental testbed is fully reproducible and easy to extend with new methods.
This way we propose the first standardized approach to conducting experiments
in imbalanced data streams that can be used by other researchers to create
trustworthy and fair evaluation of newly proposed methods. Our experimental
framework can be downloaded from
https://github.com/canoalberto/imbalanced-streams
Rethinking data and rebalancing digital power
This report highlights and contextualises four cross-cutting interventions with a strong potential to reshape the digital ecosystem:
1. Transforming infrastructure into open and interoperable ecosystems.
2. Reclaiming control of data from dominant companies.
3. Rebalancing the centres of power with new (non-commercial) institutions.
4. Ensuring public participation as an essential component of technology policymaking.
The interventions are multidisciplinary and they integrate legal, technological, market and governance solutions. They offer a path towards addressing present digital challenges and the possibility for a new, healthy digital ecosystem to emerge.
What do we mean by a healthy digital ecosystem? One that privileges people over profit, communities over corporations, society over shareholders. And, most importantly, one
where power is not held by a few large corporations, but is distributed among different and diverse models, alongside people who are represented in, and affected by the data
used by those new models. The digital ecosystem we propose is balanced, accountable and sustainable, and imagines new types of infrastructure, new institutions and new governance models that can make data work for people and society.
Some of these interventions can be located within (or built from) emerging and recently adopted policy initiatives, while others require the wholesale overhaul of regulatory
regimes and markets. They are designed to spark ideas that political thinkers, forward-looking policymakers, researchers, civil society organisations, funders and ethical innovators in the private sector consider and respond to when designing future regulations, policies or initiatives around data use and governance.
This report also acknowledges the need to prepare the ground for the more ambitious transformation of power relations in the digital ecosystem. Even a well-targeted
intervention won't change the system unless it is supported by relevant institutions and behavioural change
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