Technical Reports - Query Results
Your query term was 'number = 2001-31'1 report found
- ÖFAI-TR-2001-31 (
168kB g-zipped PostScript file, 
645kB PDF file)Discovering Simple Rules in Complex Data: A Meta-learning Algorithm and Some Surprising Musical Discoveries
- Gerhard Widmer
- This article presents a new rule discovery algorithm named PLCG
that can find simple, robust partial rule models (sets of classification
rules) in complex data where it is difficult or impossible to find
models that completely account for all the phenomena of interest.
Technically speaking, PLCG is an ensemble learning method
that learns multiple models via some standard rule learning
algorithm, and then combines these into one final rule set
via clustering, generalization, and heuristic rule selection.
The algorithm was developed in the context of an interdisciplinary
research project that aims at
discovering fundamental principles of expressive music performance from
large amounts of complex real-world data (specifically,
measurements of actual performances by concert pianists).
The article will show that PLCG succeeds in finding some
surprisingly simple and
robust performance principles, some of which represent truly novel and
musically meaningful discoveries. A set of more systematic experiments
shows that PLCG usually discovers significantly simpler theories than
more direct approaches to rule learning (including the
state-of-the-art learning algorithm RIPPER), while striking a
compromise between coverage and precision. The experiments also
show how easy it is to use PLCG as a meta-learning strategy to
explore different parts of the space of rule models.
Keywords: machine learning, data mining, rule discovery, ensemble methods, meta-learning, partial models, expressive music performance
- This article presents a new rule discovery algorithm named PLCG
that can find simple, robust partial rule models (sets of classification
rules) in complex data where it is difficult or impossible to find
models that completely account for all the phenomena of interest.
Technically speaking, PLCG is an ensemble learning method
that learns multiple models via some standard rule learning
algorithm, and then combines these into one final rule set
via clustering, generalization, and heuristic rule selection.
The algorithm was developed in the context of an interdisciplinary
research project that aims at
discovering fundamental principles of expressive music performance from
large amounts of complex real-world data (specifically,
measurements of actual performances by concert pianists).
The article will show that PLCG succeeds in finding some
surprisingly simple and
robust performance principles, some of which represent truly novel and
musically meaningful discoveries. A set of more systematic experiments
shows that PLCG usually discovers significantly simpler theories than
more direct approaches to rule learning (including the
state-of-the-art learning algorithm RIPPER), while striking a
compromise between coverage and precision. The experiments also
show how easy it is to use PLCG as a meta-learning strategy to
explore different parts of the space of rule models.