151,197 research outputs found
On Designing a Machine Learning Based Wireless Link Quality Classifier
Ensuring a reliable communication in wireless networks strictly depends on
the effective estimation of the link quality, which is particularly challenging
when propagation environment for radio signals significantly varies. In such
environments, intelligent algorithms that can provide robust, resilient and
adaptive links are being investigated to complement traditional algorithms in
maintaining a reliable communication. In this respect, the data-driven link
quality estimation (LQE) using machine learning (ML) algorithms is one of the
most promising approaches. In this paper, we provide a quantitative evaluation
of design decisions taken at each step involved in developing a ML based
wireless LQE on a selected, publicly available dataset. Our study shows that,
re-sampling to achieve training class balance and feature engineering have a
larger impact on the final performance of the LQE than the selection of the ML
method on the selected data.Comment: accepted in PIMRC 2020. arXiv admin note: text overlap with
arXiv:1812.0885
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
Combining Multiple Clusterings via Crowd Agreement Estimation and Multi-Granularity Link Analysis
The clustering ensemble technique aims to combine multiple clusterings into a
probably better and more robust clustering and has been receiving an increasing
attention in recent years. There are mainly two aspects of limitations in the
existing clustering ensemble approaches. Firstly, many approaches lack the
ability to weight the base clusterings without access to the original data and
can be affected significantly by the low-quality, or even ill clusterings.
Secondly, they generally focus on the instance level or cluster level in the
ensemble system and fail to integrate multi-granularity cues into a unified
model. To address these two limitations, this paper proposes to solve the
clustering ensemble problem via crowd agreement estimation and
multi-granularity link analysis. We present the normalized crowd agreement
index (NCAI) to evaluate the quality of base clusterings in an unsupervised
manner and thus weight the base clusterings in accordance with their clustering
validity. To explore the relationship between clusters, the source aware
connected triple (SACT) similarity is introduced with regard to their common
neighbors and the source reliability. Based on NCAI and multi-granularity
information collected among base clusterings, clusters, and data instances, we
further propose two novel consensus functions, termed weighted evidence
accumulation clustering (WEAC) and graph partitioning with multi-granularity
link analysis (GP-MGLA) respectively. The experiments are conducted on eight
real-world datasets. The experimental results demonstrate the effectiveness and
robustness of the proposed methods.Comment: The MATLAB source code of this work is available at:
https://www.researchgate.net/publication/28197031
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Application of Machine Learning to Mortality Modeling and Forecasting
Estimation of future mortality rates still plays a central role among life insurers in
pricing their products and managing longevity risk. In the literature on mortality modeling, a wide
number of stochastic models have been proposed, most of them forecasting future mortality
rates by extrapolating one or more latent factors. The abundance of proposed models shows that
forecasting future mortality from historical trends is non-trivial. Following the idea proposed in
Deprez et al. (2017), we use machine learning algorithms, able to catch patterns that are not commonly
identifiable, to calibrate a parameter (the machine learning estimator), improving the goodness of fit
of standard stochastic mortality models. The machine learning estimator is then forecasted according
to the Lee-Carter framework, allowing one to obtain a higher forecasting quality of the standard
stochastic models. Out-of sample forecasts are provided to verify the model accuracy
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