23,166 research outputs found
Understanding Learned Models by Identifying Important Features at the Right Resolution
In many application domains, it is important to characterize how complex
learned models make their decisions across the distribution of instances. One
way to do this is to identify the features and interactions among them that
contribute to a model's predictive accuracy. We present a model-agnostic
approach to this task that makes the following specific contributions. Our
approach (i) tests feature groups, in addition to base features, and tries to
determine the level of resolution at which important features can be
determined, (ii) uses hypothesis testing to rigorously assess the effect of
each feature on the model's loss, (iii) employs a hierarchical approach to
control the false discovery rate when testing feature groups and individual
base features for importance, and (iv) uses hypothesis testing to identify
important interactions among features and feature groups. We evaluate our
approach by analyzing random forest and LSTM neural network models learned in
two challenging biomedical applications.Comment: First two authors contributed equally to this work, Accepted for
presentation at the Thirty-Third AAAI Conference on Artificial Intelligence
(AAAI-19
An Integration of FDI and DX Techniques for Determining the Minimal Diagnosis in an Automatic Way
Two communities work in parallel in model-based diagnosis:
FDI and DX. In this work an integration of the FDI and the DX communities
is proposed. Only relevant information for the identification of the
minimal diagnosis is used. In the first step, the system is divided into
clusters of components, and each cluster is separated into nodes. The
minimal and necessary set of contexts is then obtained for each cluster.
These two steps automatically reduce the computational complexity
since only the essential contexts are generated. In the last step, a signature
matrix and a set of rules are used in order to obtain the minimal
diagnosis. The evaluation of the signature matrix is on-line, the rest of
the process is totally off-line.Ministerio de Ciencia y Tecnología DPI2003-07146-C02-0
A Topological-Based Method for Allocating Sensors by Using CSP Techniques
Model-based diagnosis enables isolation of faults of a system.
The diagnosis process uses a set of sensors (observations) and a model
of the system in order to explain a wrong behaviour. In this work, a
new approach is proposed with the aim of improving the computational
complexity for isolating faults in a system. The key idea is the addition of
a set of new sensors which allows the improvement of the diagnosability
of the system. The methodology is based on constraint programming
and a greedy method for improving the computational complexity of the
CSP resolution. Our approach maintains the requirements of the user
(detectability, diagnosability,. . .).Ministerio de Ciencia y Tecnología DPI2003-07146-C02-0
Online Fault Classification in HPC Systems through Machine Learning
As High-Performance Computing (HPC) systems strive towards the exascale goal,
studies suggest that they will experience excessive failure rates. For this
reason, detecting and classifying faults in HPC systems as they occur and
initiating corrective actions before they can transform into failures will be
essential for continued operation. In this paper, we propose a fault
classification method for HPC systems based on machine learning that has been
designed specifically to operate with live streamed data. We cast the problem
and its solution within realistic operating constraints of online use. Our
results show that almost perfect classification accuracy can be reached for
different fault types with low computational overhead and minimal delay. We
have based our study on a local dataset, which we make publicly available, that
was acquired by injecting faults to an in-house experimental HPC system.Comment: Accepted for publication at the Euro-Par 2019 conferenc
Analysis of cross-correlations in electroencephalogram signals as an approach to proactive diagnosis of schizophrenia
We apply flicker-noise spectroscopy (FNS), a time series analysis method
operating on structure functions and power spectrum estimates, to study the
clinical electroencephalogram (EEG) signals recorded in children/adolescents
(11 to 14 years of age) with diagnosed schizophrenia-spectrum symptoms at the
National Center for Psychiatric Health (NCPH) of the Russian Academy of Medical
Sciences. The EEG signals for these subjects were compared with the signals for
a control sample of chronically depressed children/adolescents. The purpose of
the study is to look for diagnostic signs of subjects' susceptibility to
schizophrenia in the FNS parameters for specific electrodes and
cross-correlations between the signals simultaneously measured at different
points on the scalp. Our analysis of EEG signals from scalp-mounted electrodes
at locations F3 and F4, which are symmetrically positioned in the left and
right frontal areas of cerebral cortex, respectively, demonstrates an essential
role of frequency-phase synchronization, a phenomenon representing specific
correlations between the characteristic frequencies and phases of excitations
in the brain. We introduce quantitative measures of frequency-phase
synchronization and systematize the values of FNS parameters for the EEG data.
The comparison of our results with the medical diagnoses for 84 subjects
performed at NCPH makes it possible to group the EEG signals into 4 categories
corresponding to different risk levels of subjects' susceptibility to
schizophrenia. We suggest that the introduced quantitative characteristics and
classification of cross-correlations may be used for the diagnosis of
schizophrenia at the early stages of its development.Comment: 36 pages, 6 figures, 2 tables; to be published in "Physica A
Preferential Multi-Context Systems
Multi-context systems (MCS) presented by Brewka and Eiter can be considered
as a promising way to interlink decentralized and heterogeneous knowledge
contexts. In this paper, we propose preferential multi-context systems (PMCS),
which provide a framework for incorporating a total preorder relation over
contexts in a multi-context system. In a given PMCS, its contexts are divided
into several parts according to the total preorder relation over them,
moreover, only information flows from a context to ones of the same part or
less preferred parts are allowed to occur. As such, the first preferred
parts of an PMCS always fully capture the information exchange between contexts
of these parts, and then compose another meaningful PMCS, termed the
-section of that PMCS. We generalize the equilibrium semantics for an MCS to
the (maximal) -equilibrium which represents belief states at least
acceptable for the -section of an PMCS. We also investigate inconsistency
analysis in PMCS and related computational complexity issues
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