MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti.
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Transcript of MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti.
MINDModels in decision making & data @nalysis
Enza Messina and Francesco Archetti
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Main ActivitiesResearch Areas
o Machine Learning Algorithmso Probabilistic and Relational Modelso Optimization Under Uncertainty
o Multimedia Documento Life Scienceso Ambient Intelligenceo Finance
Applicative Domains
Faculty: Francesco Archetti Enza Messina
Guglielmo LulliPost Doc: Elisabetta FersiniPhD: Federica BargnaOthers: Daniele Toscani
Ilaria GiordaniGaia ArosioLuigi Quarenghi
Machine Learning and Relational Data
- Traditional learning methods are consistent with the classical statistical inference problem formulation istances are independent and identically distributed (i.i.d.)
aiuto!
ProbabilisticModels
LearningTechniques
SRL
ProbabilisticModels
Relational Representation
LearningTechniques
- but do not reflect the real world! We need a solution able to deal with relationships and
with uncertainty in more general terms
SL
The World is Uncertain
Graphical Models (here e.g. a Bayesian network) - model uncertainty explicitly by representing the joint distribution
Fever Ache
InfluenzaRandom Variables
Direct Influences
Propositional Model!
Real-World Data are structured
PatientID Gender Birthdate
P1 M 3/22/63
PatientID Date Physician Symptoms Diagnosis
P1 1/1/01 Smith palpitations hypoglycemic P1 2/1/03 Jones fever, aches influenza
PatientID Date Lab Test Result
P1 1/1/01 blood glucose 42 P1 1/9/01 blood glucose 45
PatientID SNP1 SNP2 … SNP500K
P1 AA AB BB P2 AB BB AA
PatientID Date Prescribed Date Filled Physician Medication Dose Duration
P1 5/17/98 5/18/98 Jones prilosec 10mg 3 months
Non- i.i.d
First-Order Logic / Relational Databases
Probabilistic Relational Models
Integrate uncertainty with relational model
Convenient language for specifying complex models “Web of influence”: subtle & intuitive reasoning
Framework for incorporating heterogeneous data by connecting related entities (consider also relation uncertainty)
New problems: Relational clustering Collective classification
Open Problems: Inference and Learning
Level
Gene Cluster
LipidHSF
Endoplasmatic
GCN4
Exp. cluster
Exp. type
Heterogeneous Information
Inference
Uncertainty, Relations, Dynamics
Cau
sal
R
ela
tio
nsh
ips
Struct
. Rel
Sequence(Hidden) Markov Model
Bayes Net DBN
PRM,RBN,SLP…
MRDM,ILP
Relational Markov Model
DPRM
Some Applications
Learning Models for Relational Data: Relational Clustering
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Document Analysis
E. Fersini, E. Messina, F. Archetti, “A probabilistic relational approach for web document clustering”, Journal of Information Processing and Management, Vol. 46, no 2, p. 117-130, 2010.
E. Fersini, E. Messina, F. Archetti. “Web page classification: A probabilistic model with relational uncertainty”. In Proc. of the 2010 Conference on Information Processing and Management of Uncertainty, 2010.
E. Fersini, E. Messina, F. Archetti, Probabilistic relational models with relational uncertainty: an early study in web page classification, IEEE WI-IAT Workshop, 2009.
Publications
1. Constraint Learning
2. Objective Function Adaptation Relational Classification:
Probabilistic Relational Models with Relational Uncertainty Conditional Random Fields
E. Fersini, E. Messina, F. Archetti, “Probabilistic relational models with relational uncertainty”, Journal of Information Processing and Management, (second revision).
Submitted
Document AnalysisE-Forensics JUdicial MAnagement by Digital Libraries Semantics
Information Extraction
Emotion Recognition
Proceedings n° ……..
Accused Name XXXXXX
Witness Name KKKKKK
Prosecutor Name -
Lawyer Name YYYYYYZZZZZZ
Meeting Date 1989
Meeting Location Civitanova Marche
Hearing Summarization
Document AnalysisE-Forensics
E. Fersini, E. Messina, F. Archetti. “Multimedia Summarization in Law Courts: A Clustering-based Environment for Browsing and Consulting Judicial Folders”. In proc. of the 10th Industrial Conference on Data Mining, 2010.
E. Fersini, G. Arosio, E. Messina, F. Archetti, “Emotion recognition in judicial domain: a multilayer SVM approach, LNAI, in Proc. of the 6th International Conference on Machine Learning and Data Mining, Leipzig, 2009.
E. Fersini, G. Arosio, E. Messina, F. Archetti, D. Toscani. Multimedia Summarization in Law Courts: An Environment for Browsing and Consulting Judicial Folders. In Proc. of the 2nd International Conference on ICT Solutions for Justice, Skopje, 2009.
E. Fersini, F. Callegaro, M. Cislaghi, R. Mazzilli, S. Somaschini, R. Muscillo, D. Pellegrini,. Managing Knowledge Extraction and Retrieval from Multimedia Contents: a Case Study in Judicial Domain. In Proc. of the 2nd International Conference on ICT Solutions for Justice, Skopje, 2009.
G. Felici, E. Fersini, E. Messina, Information extraction through constrained inference in Conditional Random Fields, AIRO 2010, september 2010.
Publications
Submitted Projects
Progetto PONeJRM - electronic Justice Relationship Management
Submitted
E. Fersini, E. Messina, F. Archetti. “Emotional States in Judicial Courtrooms: An Experimental Investigation”. Sumbitted to Journal of Speech Commiunication.
E. Fersini, E. Messina, D. Toscani, F. Archetti, M. Cislaghi. Semantics and machine learning for building the next generation of judicial case and court management systems. Submitted to the Int. Conference on Knowledge Management and Information Sharing
Life Sciences
Systems Biology Applications
Regulatory modules
TF
Gene CodingControl
DNA
RNAsingle strand
Transcription +
Human cancer
Gene expressio
n
Drug Activity
Gene drug interactionidentification of a drug treatment for a given cell line based both on drug activity pattern and gene expression profile
Learning gene regulatory networks
Modelling the pharmacology of cancer
Collaborations
14
Pharmacogenomics Application: Predict drug response to oral anticoagulation therapy (OAT)
Grouping (Profiling) patients based on their clinical and genotypic features in order to suggest doctors the correct drug dosage
Haemorragic riskThrombotic riskData of about 4000 patients:
Clinical and therapeutical data: personal patients data, medical diagnosis, therapy, INR and dosage measurements Genetic data: polymorphism of three genes: CYP2C9, VKORC1 and CYP4F2 that contribute to differences in patients’ response.
In collaboration with
Publications
E. Fersini, C. Manfredotti, E. Messina, F. Archetti Relational K-Means for Gene Expression Profiles and Drug Activity Pattern Analysis, to appear on Int. Journal of Mathematical Modelling and Algorithms.
F. Archetti, I.Giordani, L. Vanneschi, “Genetic Programming for Anticancer Therapeutic Response Prediction using the NCI-60 Dataset”, Computers & Operations Research, Vol.37, No.8, pp.1395-1405, August 2010.
E. Fersini, I.Giordani, E.Messina, F. Archetti, "Relational Clustering and Bayesian Networks for Linking Gene Expression Profiles and Drug Activity Patterns", International Workshop of Applications of Machine Learning in Bioinformatics (satellite workshop of IEEE International Conference on Bioinformatics and Biomedicine- BIBM, november 2009.
L. Vanneschi , F. Archetti, M. Castelli, I. Giordani, "Classification of Oncologic Data with Genetic Programming," Journal of Artificial Evolution and Applications, vol. 2009, Article ID 848532, 13 pages, 2009. doi:10.1155/2009/848532.
F. Archetti, I.Giordani, L. Vanneschi, “Genetic Programming for QSAR Investigation of Docking Energy”, Applied Soft Computing, Vol. 10, No. 1, pp. 170-182, issn: 1568-4946, Jan 2010.
G. Ogliari, I. Giordani, A. Mihalich, D. Castaldi, A. Di Blasio, A. Dubini, E. Messina, F. Archetti, D. Mari, Nuova classificazione clinica e Farmacogenetica per predire la dinamica dell'inr nell'anziano in tao. Giornale di gerontologia, vol. lvii; p. 495-496, issn: 0017-0305, dicembre 2009
F. Archetti, I. Giordani, E. Messina, G. Ogliari, D. Mari, "A comparison of data mining approaches in the categorization of oral anticoagulant patients", International Workshop of Applications of Machine Learning in Bioinformatics (satellite workshop of IEEE International Conference on Bioinformatics and Biomedicine- BIBM, november 2009
Submitted
F. Archetti, I.Giordani, G.Mauri, E.Messina. “A new clustering approach for learning transcriptional regulatory modules”, submitted to Int. Journal of Data Mining and Bioinformatics, (second revision).
Projects
Submitted proposals:
Associazione lotta alla trombosi - Call for applications 2010Oral Anticoagulation Therapy in the elderly and womenPartners:
Brunel University, Centre for Intelligent Data AnalysisHarvard Medical School, Biomedical Cybernetics LaboratoryUniv. of Milano, Dept. of Medical Sciences, Geriatrics UnitIst. Clinico Humanitas - Thrombosis Unit (Corrado Lodigiani, MD, PhD)Ist. Auxologico Italiano, IRCCS Centro di Ricerche e Tecnologie Biomediche,
PONHEARTDRIVE Project Coordinator: Calpark – Parco Tecnologico e Scientifico della Calabria
PRINRevealing common patterns among insuline resistance, osteoporosis and chronic inflammatory diseases by using Bayesian Networks.Project Coordinator: Università degli Studi "Magna Graecia" di CATANZARO
Ambient Intelligence
Multi-target trackingMulti-target tracking: finding the tracks of an unknown number of moving targets from noisy observations.
Exploiting relations can improve the efficiency of the tracker Monitoring relations can be a goal in itself
We model the transition probability of the system with a RDBN.
In collaboration with
A new representation modelling not only objects but also their relations A new computational strategy based on a family of Sequential Monte Carlo
methods called Particle Filter
Statistical techniques for the detection of anomalous behaviours
Cristina E. Manfredotti, Enza Messina: Relational Dynamic Bayesian Networks to Improve Multi-target Tracking. ACIVS 2009: 528-539.C. Manfredotti, E. Messina, D.J. Fleet, Relations to improve multi-target tracking in an activity recognition system. Proceedings of the International Conference on Imaging for Crime Detection and Prevention, London, 2009.
Publications
Wireless Sensor Networks Bayesian abstractions for virtual sensing through low cost data aggregation and net-Bayesian abstractions for virtual sensing through low cost data aggregation and net-
wide anomaly detectionwide anomaly detection Modelling Cluster Heads as nodes of a BNModelling Cluster Heads as nodes of a BN Inference to know sensor values also in presence of temporary faults:Inference to know sensor values also in presence of temporary faults:
Lack of communication (sensor failure or sleep)Lack of communication (sensor failure or sleep) Outlier due to sensor malfunctioningOutlier due to sensor malfunctioning
1919
CH1CH2
CH3
CH4
CH5
WSN
BN
sink
F. Archetti, E. Messina, D. Toscani and M. Frigerio - IKNOS – Inference and Knowledge in Networks Of Sensors. International Journal of Sensor Networks (IJSNet), Vol.8 No. 3, 2010
F. Chiti, R. Fantacci, , F. Archetti, E. Messina, D. Toscani, Integrated Communications Framework for Context aware Continuous Monitoring with Body Sensor Networks, IEEE Journal on Selected Areas in Communications - Wireless and Pervasive Communications for Healthcare. Volume 27, Issue 4, 2009., 2009.
D. Toscani, I. Giordani, M. Cislaghi, L. Quarenghi. Querying Sensor Data for Environmental Monitoring. Submitted to International Journal of Sensor Networks (IJSNet), 2010
D. Toscani, I. Giordani, L. Quarenghi, F. Archetti . A software Environment For Supporting Sensor Querying. Submitted to IEEE Sensors 2010 Conference, Hawaii, 2010
Publications
Submitted
Transportation & Logistics
In collaboration with:
Data Models Decisions
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PRIN MIUREnhancing the European Air Transportation SystemPartners: Università di Padova, Università di Trieste.
Projects
LENVIS - Localised environmental and health information services for all (EU-FP7)sviluppo di una rete collaborativa di supporto alle decisioni, per lo scambio di informazioni e servizi riguardanti l'ambiente e la salute
Publications
D. Toscani, L. Quarenghi, F.Bargna, F. Archetti, E. Messina, "A DSS for Assessing the Impact of Environmental Quality on Emergency Hospital Admissions", In proceedings of the WHCM 2010 - IEEE Workshop on Health Care Management, February 18-20, 2010 - Venice, Italy.
Ambient IntelligenceProjects
D. Toscani, I. Giordani, F. Bargna, L. Quarenghi, F. Archetti. A software System for Data Integration and Decision Support for Evaluation of Air Pollution Health Impact. Submitted to ICEIS 2010 - 12th International Conference on Enterprise Information Systems. Funchal, Madeira – Portugal, 2010
Submitted
INSYEME – Integrated Systems for Emergencies (MIUR - FIRB)
GREIS - Gestione del Risparmio Energetico attraverso Informazioni di Sicurezza (MIUR)
In collaboration with SAL Lab.
H-CIM Health Care through Intelligent Monitoring (MIUR) In collaboration withNOMADIS Lab.
Projects
Submitted
FP7 ICT call 6 - STREPOPENCITY Open framework for Transport Demand Management for smart and sustainable
urban mobility in an open and accessible city Project Coordinator: Consorzio Milano Ricerche
In collaboration with SAL Lab. e Imaging & Vision Lab.
FLECS – FLy’s eyes for Collaborative Surveillance – (Progetto PON)
Financial Time Series
Hidden var.: Regime
Financial Time Series & Scenario Generation
1( | )
( | )t t
t t
p x x
p z x−Transition Model
Observation Model
Markov Chain
Mixture of Gaussians(Autoregressive Process)
(Autoregressive) Hidden Markov Model
Observations: pricestxtS
tS
Regime Switching Models
t=1 t=2 t=3 t=4
24
Financial Time Series Extend state space models to more general Relational Dynamic Bayesian Networks to
account not only prices but also, through CPT, “exogenous” economic factors and unstructured information
Algorithms for managing risk tracking portfolio using all available evidence and taking into account all uncertainties
“Markets are good at gathering information from many heterogeneous sources and combining it appropriately, the same we would expect from models”
PRIN 2007 "Modelli probabilistici per la rappresentazione dell’incertezza per la definizione di metodologie di selezione del portafoglio” (Università di Bergamo, Università della Calabria)
Collaboration with Brunel University and CARISMA Research Centre:
Workshop “Application of Hidden Markov Models and Filters to Time Series Methods in Finance” , London, September 2010
Projects & Collaborations
G. Consigli, C. Manfredotti, E. Messina, A sequential learning method for tracking stochastic volatility, EURO XXIV, July 2010, Lisbon
Publications
The cooperation network
University of
Toronto
Massachusset
Institute of
Technology
Norwegian University of Science
and Technology
Brunel Univers
ity
Centre of Research and Technology
Hellas
Hungarian Academy of Sciences
CARISMA
Research Center
Harvard Medical School
SB RAS Russia
Aachen University