Presentation is loading. Please wait.

Presentation is loading. Please wait.

MIND Models in decision making & Enza Messina and Francesco Archetti.

Similar presentations


Presentation on theme: "MIND Models in decision making & Enza Messina and Francesco Archetti."— Presentation transcript:

1 MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

2 Main Activities Research Areas o Machine Learning Algorithms o Probabilistic and Relational Models o Optimization Under Uncertainty o Multimedia Document o Life Sciences o Ambient Intelligence o Finance Applicative Domains Faculty: Francesco Archetti Enza Messina Guglielmo Lulli Post Doc: Elisabetta Fersini PhD: Federica Bargna Others: Daniele Toscani Ilaria Giordani Gaia Arosio Luigi Quarenghi

3 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.) Probabilistic Models Learning Techniques SRL Probabilistic Models Relational Representation Learning Techniques - but do not reflect the real world! We need a solution able to deal with relationships and with uncertainty in more general terms SL

4 The World is Uncertain Graphical Models (here e.g. a Bayesian network) - model uncertainty explicitly by representing the joint distribution FeverAche Influenza Random Variables Direct Influences Propositional Model!

5 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

6 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 Lipid HSF Endoplasmatic GCN4 Exp. cluster Exp. type Heterogeneous Information Inference

7 Uncertainty, Relations, Dynamics Causal Relationship s Struct. Rel Sequence (Hidden) Markov Model Bayes NetDBN PRM,RBN,SLP… MRDM, ILP Relational Markov Model DPRM

8 Some Applications

9 Learning Models for Relational Data: Relational Clustering #origin_ref #destination_ref #origin_ref #destination_ref document_id class document_id class 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

10 Document Analysis E-Forensics JUdicial MAnagement by Digital Libraries Semantics Information Extraction Emotion Recognition Proceedings n° …….. Accused NameXXXXXX Witness NameKKKKKK Prosecutor Name- Lawyer Name YYYYYY ZZZZZZ Meeting Date1989 Meeting LocationCivitanova Marche Hearing Summarization

11 Document Analysis E-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 PON eJRM - 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

12 Life Sciences

13 Systems Biology Applications Regulatory modules TF Gene Coding Control DNA RNA single strand Transcription + Human cancer Gene expressio n 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 Learning gene regulatory networks Modelling the pharmacology of cancer Collaborations

14 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 risk Thrombotic risk Data 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

15 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).

16 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 and chronic inflammatory diseases by using Bayesian Networks. Project Coordinator: Università degli Studi "Magna Graecia" di CATANZARO

17 Ambient Intelligence

18 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. 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

19 Wireless Sensor Networks Bayesian abstractions for virtual sensing through low cost data aggregation and net- wide anomaly detection Bayesian abstractions for virtual sensing through low cost data aggregation and net- wide anomaly detection Modelling Cluster Heads as nodes of a BN Modelling 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 malfunctioning Outlier due to sensor malfunctioning 19 CH 1 CH 2 CH 3 CH 4 CH 5 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,, 2009. 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. 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

20 Transportation & Logistics In collaboration with: DataModelsDecisions u LufLuf j PjfPjf de st f h k ori g f v w PRIN MIUR Enhancing the European Air Transportation System Partners: Università di Padova, Università di Trieste. Projects

21 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 Intelligence Projects 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

22 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 - 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 – FLys eyes for Collaborative Surveillance – (Progetto PON)

23 Financial Time Series

24 Hidden var.: Regime Financial Time Series & Scenario Generation Transition Model Observation Model Markov Chain Mixture of Gaussians (Autoregressive Process) (Autoregressive) Hidden Markov Model Observations: prices Regime Switching Models 24

25 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 dellincertezza 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

26 The cooperation network University of Toronto Massachusset Institute of Technology Norwegian University of Science and Technology Brunel University Centre of Research and Technology Hellas Hungarian Academy of Sciences CARISMA Research Center Harvard Medical School SB RAS Russia Aachen University


Download ppt "MIND Models in decision making & Enza Messina and Francesco Archetti."

Similar presentations


Ads by Google