The MIP group has focussed on the learning
of comprehensible models on different paradigms: Functional Logic
Programs (FLP), decision trees and decision lists.
The group was formed in 1997 with the
initial goal of extending ILP to other declarative languages, and,
since then, has been acquiring a broader view of the field, exploring
different techniques and applications in machine learning (such
as evolutionary programming) and data mining.
Currently, the research areas are:
Multi-paradigm Inductive Programming
Machine Learning and Data Mining
Knowledge Discovery
ROC analysis, cost-sensitive
learning and model evaluation for decision support
Inductive Debugging.
Nonetheless, the primary interest is still
the learning of comprehensible or declarative models from data.
Learning
Systems:
We have developed two learning systems:
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.
Publications
and Reports:
Documents are ordered by date (more recent
first).
Hernández-Orallo,
J.; Ramírez-Quintana,
M.J.The
role of induction in (semi-)automated life-cycles. in 9th Intl
Workshop on Functional and Logic Programming, WFLP'2000, pages 283-295,
2000.
