Research on Data mining at Research Group in Intelligent Systems

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GRSI focuses on the research and development of intelligent systems based on (1) extracting interesting patterns from moderate and large complex data (Data Mining) and (2) learning from them (Machine Learning) for helping experts by means of the building of decision support systems. In this framework, GRSI works on different stages of the process of data mining: pre-processing, characterization of data sets, analysis for a better understanding and improvement of machine learning techniques, methodologies to evaluate learners, and post-processing. During the last few years, the research has mainly focused on learning methods inspired by natural principles and analogy. The group is known for its expertise on Evolutionary Computation, Soft Case-Based Reasoning, and Neural Networks.

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Research on Data mining at Research Group in Intelligent Systems

  1. 1. Data Mining gResearch, Innovation and Development forsupporting experts in their decisions pp g p Supporting experts is helping them to take more effective, effective efficient and reliable decisions Research, Innovation and Development for Page 1 supporting experts in their decisions
  2. 2. Campus laSalle Barcelona p • Campus with 5 buildings p g • 4000 students. • More than 100 years training highly qualified professionals. Entrepreneurship Research Differential ff Groups methodology Prestige and International innovation character La Salle Laboratories and Technova infrastructures Barcelona Research, Innovation and Development for Page 2 supporting experts in their decisions
  3. 3. Outline• Research Group in Intelligent Systems – Descriptors – Research on Data Mining• Projects related to Health Sciences – Decision support system for Breast Cancer – Data Mining as support for melanoma experts Research, Innovation and Development for Page 3 supporting experts in their decisions
  4. 4. What is Artificial Intelligence ( ) g (AI)? • John McCarthy coined AI term in 1956 as ‘the science and engineering of making intelligent machines’ at a conference at Dartmouth College. Intelligent machine terms refer to the capability of performing intelligent human processes as: – Learning – Reasoning – Problem solving – Perception – Language understanding • AI has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science. – Prediction – Classification – Regression – Clustering – F ti ti i ti Function optimization Research, Innovation and Development for Page 4 supporting experts in their decisions
  5. 5. Why is AI p y powerful? • The power resides in the combination of disciplines that tackle the same problems as AI: learn and understand, to solve problems and to make decisions. • AI is fed from many disciplines – Phil Philosophy: L i methods of reasoning, mind as physical system, h Logic, th d f i i d h i l t foundations of learning, language, rationality. – Mathematics: Formal representation and proof, algorithms, computation, (un)decidability, (in)tractability. ( ) ( ) – Statistics : Modeling uncertainty, learning from data. – Economics: Utility, decision theory, rational economic agents. y y g – Neuroscience: Neurons as information processing units. – Psychology / NeuroScience: How do people behave, perceive, process cognitive information, represent knowledge information knowledge. – Computer Engineering: Building fast computers. – Control Theory: Design systems that maximize an objective function over time. – Linguistics: Knowledge representation, grammars. Research, Innovation and Development for Page 5 supporting experts in their decisions
  6. 6. Basis of Artificial Intelligence g Philosophy Mathematical. Computational Cognitive Computational • Discussion about • Philosophic bases linguistic psychology engineering the th possibility of a ibilit f requires f i formall • Understanding • Behavior theories, • Some mechanism, mechanical rules. language requires rational behave hardware and tools intelligence. understanding of basis. are required for AI. the subject matter and th context. d the t t Research, Innovation and Development for Page 6 supporting experts in their decisions
  7. 7. A possible map of the current AI p p • Non monotonic reasoning • Evolutionary Computation • Model based reasoning • Case-Based Reasoning • Constraint ti f ti C t i t satisfaction •R i f Reinforcement Learning tL i • Qualitative reasoning • Neural Network • Uncertain reasoning • Data Analysis • Temporal reasoning • Heuristic search Machine Reasoning Learning Robotics, perception Knowledge and natural • Logic Management language g g • Multiagents systems processing • Decision Support System • Knowledge management • Robotics and control • Knowledge representation • Natural Language Processing • Ontology and semantic web • Artificial vision • Computer-Human interaction • Speech recognition Research, Innovation and Development for Page 7 supporting experts in their decisions
  8. 8. Research Group in Intelligent Systems p g y• GRSI is a research group focused on Machine Learning, especially in the field of Knowledge Discovery from Databases (KDD) (also known as Data Mining) for extracting interesting patterns from moderate and large complex data. – Created in 1994 – Recognized as consolidated by Generalitat de Catalunya since 2002. – Group is composed of 18 members. – F ll professor J Full f Josep M í G María Garrell i th head of th group. ll is the h d f the– We tackle classification, prediction, regression, optimization, recommendation and diagnosis problems which occur in complex and huge volume of data in domains such as…. Health Energy Telematic Learning Research, Innovation and Development for Page 8 supporting experts in their decisions
  9. 9. 9Data Mining sets the difference g Value Wisdom How can we help them? (Knowledge+ experience) ( g ) Knowledge Why they are getting worse?How far do you (Information (Information+ rules) want to go? Information How many patients got worse? (Data + Context) How many patients are in the Data Intensive Care Unit? KDD allow experts to extract useful and hidden knowledge from data. The approach is valid for any domain Volume Business, space, communication media, insurance companies, financial services, health sciences, games, etc. Research, Innovation and Development for Page 9 supporting experts in their decisions
  10. 10. Research lines• GRSI works on the different stages of Knowledge Data Discovery: characterization, pre-processing, analysis for a better understanding and improvement of machine learning techniques, methodologies to evaluate learners and post-processing. Problem Data Analysis Analysis Data D t Processing Production Knowledge Modeling Evaluation Research, Innovation and Development for Page 10 supporting experts in their decisions
  11. 11. Data Mining applications g pp Clustering Classification Knowledge Association rules discovery Regression Research, Innovation and Development for Page 11 supporting experts in their decisions
  12. 12. Techniques q Create C t computer t Solves S l new problemsbl Simulate Si l t some Measure th M the Neura Networks s Complex metrics s omputation n Soft Case-Based Reasoning g programs inspired by using other previously properties of biological ‘complexity’ of a the process of natural solved. neural networks to problem in terms of selection and genetic replicate how ‘our class separability and xity E.g. Retrieve a set of g al laws for search search, neurons works. neurons’ works the discriminant powerEvolutionary Co similar mammographic optimization and images to a expert E.g. Build system that of features. machine learning. according to a set of is able to replicate a E.g. Relate how the E.g. Look for the best criteria. behavior based on a data complexity affects equation that set of inputs and f the performance of f f C represents a set of outputs previously algorithms in order to points. known. adjust them properly. Research, Innovation and Development for Page 12 supporting experts in their decisions
  13. 13. GRSI members Director Member emeriti Garrell Guiu Josep M PhD Guiu, M., Bacardit, Jaume, PhD, Bacardit Jaume PhD UK Assistant executive director Castanys Tutzó, Mireia, PhD Fornells Herrera, Albert, PhD Farguell Matesanz, Enric, PhD Members M b Llorà, Xavier, PhD, Ll à X i PhD USA Bernadó Mansilla, Ester, PhD Martorell Rodon, Josep Maria, PhD Camps Dausà, Joan p , Macià Antolínez, Núria, PhD Corral Torruella, Guiomar, PhD Nettleton, David, PhD García Piquer, Álvaro Orriols-Puig, Albert, PhD, USA Garriga Berga Carles PhD Berga, Carles, Salamó Llorente Maria PhD Llorente, Maria, Golobardes Ribé, Elisabet, PhD Pazienza de Filippis, Giovanni Egidio, Nicolàs Sans, Rubén PhD, Hungary Rios Boutín, Joaquim Sancho Asensio, Andreu Teixidó Navarro, Francesc Navarro Vernet Bellet, David Research, Innovation and Development for Page 13 supporting experts in their decisions
  14. 14. Outline• Research Group in Intelligent Systems – Descriptors – Research on Data Mining• Projects related to health Sciences – Decision support system for Breast Cancer – Data Mining as support for melanoma experts Research, Innovation and Development for Page 14 supporting experts in their decisions
  15. 15. Breast cancer diagnosis g TIC2002-04160-C02-02• Goal: Development of a tool for intelligent retrieval of mammographic images by content analysis in order to help experts in the diagnosis process. Digitalization, Di it li ti Retrieval ofMammographic capture segmentation and feature Diagnosis support mammographic records extraction Research, Innovation and Development for Page 15 supporting experts in their decisions
  16. 16. Melanoma diagnosis g TIN2006-15140-C03-03• Goal: Help experts in the melanoma characterization for improving melanoma diagnosis Characterization Patterns Medical protocols Decision support systems pp y Research, Innovation and Development for Page 16 supporting experts in their decisions
  17. 17. Telematic vulnerabilities TIN2006-15140-C03-03 • Goal: Provide tools to the security analyst for helping them in the security analysis tasks by means of the identification of problematic situations which are not obviously. CONSENSUS ANALIA Research, Innovation and Development for Page 17 supporting experts in their decisions
  18. 18. Active Demand Management g CEN200710126 • Goal: Integration of system (OS), distribution (OD) and sellers (CM) agents for an efficient management of demand demand. Design and OS implementation of agent Design and develop communication intelligent devices for the g management of energy demand at homeOD GAD CM Develop of specific rates CL Pattern identification Analysis of client demand for clients Research, Innovation and Development for Page 18 supporting experts in their decisions
  19. 19. 19 Integris: INTElligent GRId Sensor communications FP7 ICT-Energy-2009-1, Objective 6.5, #247938• Goal: Integration and management of communication technologies in smart- grids for assuring QoS, security and reliability. Research, Innovation and Development for Page 19 supporting experts in their decisions
  20. 20. Thanks for your attention y For further information visit http://www.salle.url.edu/GRSI or send an email to afornells@salle url edu afornells@salle.url.edu Research, Innovation and Development for Page 20 supporting experts in their decisions

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