The DMIP team began in 1997, and consolidated as a group in 2000, with the
initial goal of extending ILP to other declarative languages.
Since then, the group has been acquiring a broader view of the field, exploring
different techniques and applications, focussing on the evaluation of machine learning and machine intelligence systems, so spanning in many areas of machine learning, data mining and artificial intelligence.
Currently, the research areas are:
Machine Learning and Data Mining
Multi-paradigm Inductive Programming
ROC analysis, cost-sensitive
learning and model evaluation for decision support
Agent Intelligence Evaluation.
MML induction and Solomonoff prediction.
Probabilistic (inductive) programming.
Nonetheless, the primary interest is still
the learning of comprehensible or declarative models from data and the understanding of system performance.
The FLIP system (1998-2001, click here for downloading the software and for
more information): implements a framework for the Induction of
Functional Logic Programs (IFLP) from facts. This can be seen as an
extension to the now consolidated field of Inductive Logic Programming
(ILP). Inspired in the inverse resolution operator of ILP, the system
is based on the reversal of narrowing, the more usual operational
mechanism for Functional Logic Programming. The main advantages of the FLIP system over the most used ILP systems are a
natural handling of functions, without the use of mode or determinism
declarations, and its power for inducing short recursive programs. Its
applications are mainly program synthesis, program debugging and data
mining of small highly structured documents.
The SMILES system (2001-2002, click here for downloading the software and for
more information): a machine learning system that integrates many
different features from other machine learning techniques and paradigms
and, more importantly, it presents several innovations in almost all of
these features. In particular, SMILES extends classical decision tree learners in
many ways (new splitting criteria, non-greedy search, new partitions,
extraction of several and different solutions), it has an anytime
handling of resources, and has a sophisticated and quite effective
handling of costs. In this way, SMILES combines and improves the recent interest in
hypotheses combination (e.g. boosting) and cost-sensitive learning (a
priori and a posteriori class assignments, ROC analysis) outperforming
previous systems in many situations. Its applications are basically
data-mining and any other machine learning task where decision trees
could be useful.
The DBDT system (2004-2010, click here for downloading the software and for
more information): is a machine learning algorithm that integrates decision tree learning and center splitting.
Roughly speaking, the inferred classifer can be viewed as a tree of attribute prototypes (The value distribution
of an attribute is represented by a set of prototypes.). An instance is linked to one prototype or other depending
on its proximity.
A machine intelligence testing framework system (2010-2012), click here for downloading the software. For more information, see the project Anytime Universal Intelligence .
A RLGGP system (2011-2012), a system that integrates reinforcement learning and General Game Playing, click here for more information.
A MML-LPP-Cost system (2011-2012), a system for coding logic programs with probabilities using MML, click here for more information.
Newton Trees, stochastic distance-based decision trees (2010-2012), click here for more information.
AirVLC: An application for producing real-time urban air pollution forecasts for the city of Valencia in Spain AirVLC (2015-).
Multidimensional: With this software, multidimensional data is systematically analysed at multiple granularities by applying aggregate and disaggregate operators (e.g., by the use of OLAP tools). We show how these strategies behave when the resolution context changes, using several machine learning techniques in four application domains (link)(2015)
Coverage Graphs : Knowledge acquisition with forgetting: an incremental and developmental rule-based setting (Expermiments & Code in GitHub). (2014-2016)
IRT in machine learning : Making sense of IRT in machine learning (Expermiments & Code in GitHub). (2016)