11,268 research outputs found
Robust artificial neural networks and outlier detection. Technical report
Large outliers break down linear and nonlinear regression models. Robust
regression methods allow one to filter out the outliers when building a model.
By replacing the traditional least squares criterion with the least trimmed
squares criterion, in which half of data is treated as potential outliers, one
can fit accurate regression models to strongly contaminated data.
High-breakdown methods have become very well established in linear regression,
but have started being applied for non-linear regression only recently. In this
work, we examine the problem of fitting artificial neural networks to
contaminated data using least trimmed squares criterion. We introduce a
penalized least trimmed squares criterion which prevents unnecessary removal of
valid data. Training of ANNs leads to a challenging non-smooth global
optimization problem. We compare the efficiency of several derivative-free
optimization methods in solving it, and show that our approach identifies the
outliers correctly when ANNs are used for nonlinear regression
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
A survey of outlier detection methodologies
Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review
Practical Bayesian optimization in the presence of outliers
Inference in the presence of outliers is an important field of research as
outliers are ubiquitous and may arise across a variety of problems and domains.
Bayesian optimization is method that heavily relies on probabilistic inference.
This allows outstanding sample efficiency because the probabilistic machinery
provides a memory of the whole optimization process. However, that virtue
becomes a disadvantage when the memory is populated with outliers, inducing
bias in the estimation. In this paper, we present an empirical evaluation of
Bayesian optimization methods in the presence of outliers. The empirical
evidence shows that Bayesian optimization with robust regression often produces
suboptimal results. We then propose a new algorithm which combines robust
regression (a Gaussian process with Student-t likelihood) with outlier
diagnostics to classify data points as outliers or inliers. By using an
scheduler for the classification of outliers, our method is more efficient and
has better convergence over the standard robust regression. Furthermore, we
show that even in controlled situations with no expected outliers, our method
is able to produce better results.Comment: 10 pages (2 of references), 6 figures, 1 algorith
Autoencoders for strategic decision support
In the majority of executive domains, a notion of normality is involved in
most strategic decisions. However, few data-driven tools that support strategic
decision-making are available. We introduce and extend the use of autoencoders
to provide strategically relevant granular feedback. A first experiment
indicates that experts are inconsistent in their decision making, highlighting
the need for strategic decision support. Furthermore, using two large
industry-provided human resources datasets, the proposed solution is evaluated
in terms of ranking accuracy, synergy with human experts, and dimension-level
feedback. This three-point scheme is validated using (a) synthetic data, (b)
the perspective of data quality, (c) blind expert validation, and (d)
transparent expert evaluation. Our study confirms several principal weaknesses
of human decision-making and stresses the importance of synergy between a model
and humans. Moreover, unsupervised learning and in particular the autoencoder
are shown to be valuable tools for strategic decision-making
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