Research Group on Intelligent Data Analysis Laboratory

Description

The main purpose of IDAL is the study and application of intelligent methods of data analysis for pattern recognition, with applications that struggle with prediction, classification or trend determination.

Its members apply classic statistical methods and automatic learning techniques to large databases: statistical hypothesis testing, linear models, feature selection and extraction, neural networks, clustering algorithms, decision trees, support vector machines, probabilistic graphical models, manifold visualization, fuzzy logic, reinforcement learning, etc.

The ultimate goal of the application of these methods is to generate mathematical models which enable the optimization of processes and resources, as well as to reach the optimal decision making stage. A clear example of this is the area of health, where IDAL has developed clinical decision support application based on data analysis. These applications make it possible to improve the patient’s quality of life (establishing optimal clinical guidelines) while reducing healthcare costs.

Complementing this knowledge, the group has extensive experience in signal processing (spectral analysis, digital filter, adaptive process, etc.) due to their work of over 10 years in biosignal processing (mainly ECG and EEG). With all this background, IDAL is able to analyse a wide range of data and signals. This fact is backed up by the large number of both private and public contracts it has developed in different areas of knowledge. Furthermore, most of the practical work carried out has been displayed in important scientific publications with high impact parameters and in a large number of communications to international congresses within the area of data analysis.

Among the developed applications, (outside the health area already mentioned) are the following, i.a: web recommendations, models for optimal incentive management to gain customer loyalty, measurement-based shoe recommendations, and other data analysis consultancy works. In addition to its practical work IDAL, it develops new data analysis algorithms improving the performance of the existing ones. This research work is also reflected in a wide dissemination in the form of different publications in journals of impact and in congresses of data analysis relevant to the scientific community.

Goals CT

Advanced data mining.
Knowledge extraction of large databases.
Expert system application with real applications.
Development of new database analysis algorithms
Big data.

Research lines

Quantum machine learning
Use of formalism of quantum mechanics to improve the performance of machine learning algorithms. Use of machine learning for the description and extraction of quantum phenomena knowledge.
Intelligent data analysis
Application of automatic learning techniques for problems with prediction, classification and recognition of patterns or trends.
Big Data
Large database analysis in which there are three characteristics that make them special: growth velocity, variety in the data classes and volume.
Process optimisation
Development of reinforcement learning models and dynamic programming for cost reduction, the improvement of important parameters and the increase of efficiency.
Signal capture and processing
Development of equipment and algorithms custom-made for their aqcuisition and signal processing.
Natural Language Processing
Extraction of structured information and knowledge from the analysis of free texts and a priori unstructured information.
Recommender system
Development of product recommendation engines based on the characteristics of the customer and management of personalised promotions.

Management

SERRANO LOPEZ, ANTONIO JOSE
PDI-Titular d'Universitat
Coordinador/a Curs

Research Group on Intelligent Data Analysis Laboratory - IDAL