Method Trees: Building Blocks for Self-Organizable Representations of Value Series

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Ingo_Mierswa:gecco05ws,

• author = "Ingo Mierswa and Katharina Morik",
• title = "Method Trees: Building Blocks for Self-Organizable Representations of Value Series",
• booktitle = "Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program",
• year = "2005",
• month = "25-29 " # jun,
• 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",
• publisher = "ACM Press",
• address = "Washington, D.C., USA",
• keywords = "genetic algorithms, genetic programming",
• pages = "293--300",
• URL = "http://gpbib.cs.ucl.ac.uk/gecco2005wks/papers/0293.pdf",
• 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",

}

Genetic Programming entries for Ingo Mierswa Katharina Morik

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