Chasing Undetected Fraud Processes with Deep Probabilistic Networks

2019 ◽  
pp. 97-116
Author(s):  
Christophe Thovex
Keyword(s):  
Probabilistic Networks

Causal Probabilistic Network and Power Spectral Estimation Used in Sleep Stage Classification

1997 ◽  
Vol 36 (04/05) ◽  
pp. 41-46
Author(s):  
A. Kjaer ◽  
W. Jensen ◽  
T. Dyrby ◽  
L. Andreasen ◽  
J. Andersen ◽  
...  
Keyword(s):  
Sleep Stage ◽  
Spectral Estimation ◽  
Interrater Agreement ◽  
Probabilistic Networks ◽  
Probabilistic Network ◽  
Spectral Bands ◽  
Power Spectral ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
The Brain

Abstract.A new method for sleep-stage classification using a causal probabilistic network as automatic classifier has been implemented and validated. The system uses features from the primary sleep signals from the brain (EEG) and the eyes (AOG) as input. From the EEG, features are derived containing spectral information which is used to classify power in the classical spectral bands, sleep spindles and K-complexes. From AOG, information on rapid eye movements is derived. Features are extracted every 2 seconds. The CPN-based sleep classifier was implemented using the HUGIN system, an application tool to handle causal probabilistic networks. The results obtained using different training approaches show agreements ranging from 68.7 to 70.7% between the system and the two experts when a pooled agreement is computed over the six subjects. As a comparison, the interrater agreement between the two experts was found to be 71.4%, measured also over the six subjects.

scholarly journals Stochastic Constraint Propagation for Mining Probabilistic Networks

2019 ◽  
Author(s):  
Anna Louise D. Latour ◽  
Behrouz Babaki ◽  
Siegfried Nijssen
Keyword(s):  
Data Mining ◽  
Probability Distributions ◽  
Constraint Propagation ◽  
Mixed Integer ◽  
Test Cases ◽  
Decision Diagrams ◽  
Probabilistic Networks ◽  
Ordered Binary Decision Diagrams ◽  
Binary Decision ◽  
Domain Consistency

A number of data mining problems on probabilistic networks can be modeled as Stochastic Constraint Optimization and Satisfaction Problems, i.e., problems that involve objectives or constraints with a stochastic component. Earlier methods for solving these problems used Ordered Binary Decision Diagrams (OBDDs) to represent constraints on probability distributions, which were decomposed into sets of smaller constraints and solved by Constraint Programming (CP) or Mixed Integer Programming (MIP) solvers. For the specific case of monotonic distributions, we propose an alternative method: a new propagator for a global OBDD-based constraint. We show that this propagator is (sub-)linear in the size of the OBDD, and maintains domain consistency. We experimentally evaluate the effectiveness of this global constraint in comparison to existing decomposition-based approaches, and show how this propagator can be used in combination with another data mining specific constraint present in CP systems. As test cases we use problems from the data mining literature.

Social and Affective Agents to Motivate Collaboration on Agent-Based Intelligent Tutoring Systems

2008 ◽  
pp. 201-227
Author(s):  
Elisa Boff ◽  
Cecília Dias Flores
Keyword(s):  
Learning Style ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Affective State ◽  
Educational Systems ◽  
Probabilistic Networks ◽  
Tutoring Systems ◽  
Agent Based ◽  
Social Agent ◽  
The Social

This chapter presents a social and affective agent, named social agent, that has been modeled using probabilistic networks in order to support and motivate collaboration in an intelligent tutoring system (ITS). The social agent suggests to students a workgroup to join in. Our testbed ITS is called AMPLIA, a probabilistic multiagent environment to support the diagnostic reasoning development and the diagnostic hypotheses modeling of domains with complex and uncertain knowledge, as the medical area. The AMPLIA environment is one of the educational systems, integrated in Portedu, which is a Web portal that provides access to educational contents and systems. The social agent belongs to Portedu platform and it is used by AMPLIA. The social agent reasoning is based on individual aspects, such as learning style, performance, affective state, personality traits, and group aspects, as acceptance and social skills. The chapter also presents some experiments using AMPLIA, and results obtained by the social agent.

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