@inproceedings{Ingo_Mierswa:gecco05ws,
title = {Method Trees: Building Blocks for Self-Organizable
Representations of Value Series},
address = {Washington, D.C., USA},
author = {Ingo Mierswa and Katharina Morik},
booktitle = {Genetic and Evolutionary Computation Conference
{(GECCO2005)} workshop program},
editor = {Franz Rothlauf and Misty Blowers and J{\"u}rgen Branke and Stefano Cagnoni and Ivan I. Garibay and Ozlem Garibay and J{\"o}rn Grahl and Gregory Hornby and Edwin D. {de Jong} and Tim Kovacs and Sanjeev Kumar and Claudio F. Lima and Xavier Llor{\`a} and Fernando Lobo and Laurence D. Merkle and Julian Miller and Jason H. Moore and Michael O'Neill and Martin Pelikan and Terry P. Riopka and Marylyn D. Ritchie and Kumara Sastry and Stephen L. Smith and Hal Stringer and Keiki Takadama and Marc Toussaint and Stephen C. Upton and Alden H. Wright},
month = {25-29 June},
pages = {293--300},
publisher = {ACM Press},
url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0293.pdf},
year = {2005},
abstract = {We introduce a framework for automatic feature
extraction from very large series. The extracted
features build a new representation which is better
suitable for a given learning task. The development of
appropriate feature extraction methods is a tedious
effort, particularly because every new classification
task requires tailoring the feature set anew.
Therefore, the simple building blocks defined in our
framework can be combined to complex feature extraction
methods. We employ a genetic programming approach
guided by the performance of the learning classifier
using the new representation. Our approach to evolve
representations from series data requires a balance
between the completeness of the methods on one side and
the tractability of searching for appropriate methods
on the other side. Some theoretical considerations
illustrate the trade-off. After the feature extraction,
a second process learns a classifier from the
transformed data. The practical use of the methods is
shown by two types of experiments in the domain of
music data classification: classification of genres and
classification according to user preferences.},
notes = {Distributed on CD-ROM at GECCO-2005. ACM
1-59593-097-3/05/0006},
keywords = {algorithms, genetic programming }
}
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