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MIND
Models in decision making & data @nalysis
Enza Messina and Francesco Archetti
Main Activities
Research Areas
o Machine Learning Algorithms
o Probabilistic and Relational Models
o Optimization Under Uncertainty
o
o
o
o
Applicative Domains
Multimedia Document
Life Sciences
Ambient Intelligence
Finance
Faculty:
Post Doc:
PhD:
Others:
Francesco Archetti
Enza Messina
Guglielmo Lulli
Elisabetta Fersini
Federica Bargna
Daniele Toscani
Ilaria Giordani
Gaia Arosio
Luigi 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.)
but do not reflect the real world!
 We need a solution able to deal with relationships and
with uncertainty in more general terms
Probabilistic
Models
SL
Learning
Techniques
Probabilistic
Models
Learning
Techniques
SRL
Relational
Representation
The World is Uncertain
Graphical Models (here e.g. a Bayesian network) - model uncertainty explicitly by
representing the joint distribution
Fever
Ache
Random Variables
Direct Influences
Influenza
Propositional Model!
Real-World Data are structured
Non- i.i.d
PatientID Gender Birthdate
P1
M
3/22/63
PatientID Date Physician Symptoms
P1
P1
1/1/01
2/1/03
Smith
Jones
palpitations hypoglycemic
fever, aches influenza
First-Order
Logic / Relational
Lab Test
Result
PatientID SNP1 SNP2
Databases
PatientID Date
P1
P1
1/1/01 blood glucose
1/9/01 blood glucose
42
45
PatientID Date Prescribed Date Filled
P1
5/17/98
5/18/98
P1
P2
Diagnosis
AA
AB
… SNP500K
AB
BB
BB
AA
Physician Medication Dose
Jones
prilosec
10mg
Duration
3 months
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
Heterogeneous
Information
L N
E E
A R
R
Gene Cluster
Exp. type
GCN4
HSF
Lipid
Exp. cluster
Endoplasmatic
Level

Open Problems: Inference and Learning
Inference
Uncertainty, Relations, Dynamics
DPRM
Causal
Relationship
s
PRM,RBN,SLP…
Bayes Net
MRDM,
ILP
DBN
Relational
Markov Model
Sequence
(Hidden)
Markov Model
Some Applications
Document Analysis

Learning Models for Relational Data:

Relational Clustering
1. Constraint Learning
2. Objective Function Adaptation
 Relational Classification:


Probabilistic Relational Models with Relational Uncertainty
Conditional Random Fields
Document
Link
♦ document_id
class
Rlv
#origin_ref
#destination_ref
Publications
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.
Submitted
E. Fersini, E. Messina, F. Archetti, “Probabilistic relational models with relational uncertainty”, Journal of Information Processing and
Management, (second revision).
Document Analysis
E-Forensics

JUdicial MAnagement by Digital Libraries Semantics
Information Extraction
Hearing Summarization
Proceedings n° ……..
Accused Name
XXXXXX
Witness Name
KKKKKK
Prosecutor Name
-
Lawyer Name
YYYYYY
ZZZZZZ
Meeting Date
1989
Meeting Location
Civitanova Marche
Emotion Recognition
Document Analysis
E-Forensics
Publications
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.
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
Submitted Projects
 Progetto PON
eJRM - electronic Justice Relationship Management
Life Sciences
Systems Biology Applications
Learning gene regulatory networks
Gene
DNA
Control
Coding
+
Transcription
RNA
single
strand
Regulatory modules
Modelling the pharmacology of cancer
Human
cancer
Gene
expressio
n
Collaborations
Drug
Activity
Gene drug interaction
identification of a drug treatment for a given cell
line based both on drug activity pattern and gene
expression profile
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
Data of about 4000 patients:
Haemorragic risk
Thrombotic risk
 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
14
Publications
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
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.
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 2010
Oral Anticoagulation Therapy in the elderly and women
Partners:
Brunel University, Centre for Intelligent Data Analysis
Harvard Medical School, Biomedical Cybernetics Laboratory
Univ. of Milano, Dept. of Medical Sciences, Geriatrics Unit
Ist. Clinico Humanitas - Thrombosis Unit (Corrado Lodigiani, MD, PhD)
Ist. Auxologico Italiano, IRCCS Centro di Ricerche e Tecnologie Biomediche,
 PON
HEARTDRIVE
Project Coordinator: Calpark – Parco Tecnologico e Scientifico della Calabria
 PRIN
Revealing
common patterns among insuline resistance, osteoporosis
diseases by using Bayesian Networks.
Project Coordinator: Università degli Studi "Magna Graecia" di CATANZARO
and
chronic
inflammatory
Ambient Intelligence
Multi-target tracking
Multi-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.

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

Publications
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.
In collaboration with
Wireless Sensor Networks



Bayesian abstractions for virtual sensing through low cost data aggregation and netwide anomaly detection
Modelling Cluster Heads as nodes of a BN
Inference to know sensor values also in presence of temporary faults:


Lack of communication (sensor failure or sleep)
Outlier due to sensor malfunctioning
CH2
CH1
BN
CH5
CH4
sink
CH3
WSN
Publications
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.
Submitted
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
19
Transportation & Logistics
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Data
Models
Decisions
In collaboration with:
Projects
PRIN MIUR
Enhancing the European Air Transportation System
Partners: Università di Padova, Università di Trieste.
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Ambient Intelligence
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.
Submitted
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
Projects
In collaboration with SAL Lab.
INSYEME – Integrated Systems for Emergencies (MIUR - FIRB)
GREIS - Gestione del Risparmio Energetico attraverso Informazioni di Sicurezza (MIUR)
In collaboration withNOMADIS Lab.
H-CIM Health Care through Intelligent Monitoring (MIUR)
Submitted
FP7 ICT call 6 - STREP
OPENCITY 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
Financial Time Series & Scenario
Generation

Regime Switching Models

Observations: prices
St

Hidden var.: Regime
xt
Transition Model
p( xt | xt 1 )
Markov Chain
Observation Model
p( zStt | xt )
Mixture of Gaussians
(Autoregressive Process)
(Autoregressive) Hidden Markov Model
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”
Publications
G. Consigli, C. Manfredotti, E. Messina, A sequential learning method for tracking stochastic volatility, EURO XXIV, July
2010, Lisbon
Projects & Collaborations
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
The cooperation network
University
of Toronto
Brunel
University
CARISMA
Research
Center
Norwegian University of
Science and Technology
SB RAS Russia
Aachen University
Massachusset
Institute of
Technology
Harvard
Medical
School
Hungarian Academy
of Sciences
Centre of Research
and Technology
Hellas