- author = "Ilknur Icke and Andrew Rosenberg",
- title = "Dimensionality reduction using symbolic regression",
- booktitle = "GECCO 2010 Late breaking abstracts",
- year = "2010",
- editor = "Daniel Tauritz",
- isbn13 = "978-1-4503-0073-5",
- keywords = "genetic algorithms, genetic programming",
- pages = "2085--2086",
- month = "7-11 " # jul,
- organisation = "SIGEVO",
- address = "Portland, Oregon, USA",
-
DOI = "
doi:10.1145/1830761.1830874",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- abstract = "In this paper, we propose a symbolic regression approach for data visualisation that is suited for classification tasks. Our algorithm seeks a visually and semantically interpretable lower dimensional representation of the given dataset that would increase classifier accuracy as well. This simultaneous identification of easily interpretable dimensionality reduction and improved classification accuracy relieves the user of the burden of experimenting with the many combinations of classification and dimensionality reduction techniques",
-
notes = "Flubber, ECJ, WEKA, UCI wisconsin breast,
leptographsus crabs. Compare with PCA, MDS and random
projections. no significant improvement.
Also known as \cite{1830874} Distributed on CD-ROM at GECCO-2010.
ACM Order Number 910102.",
