Multi-Objective Genetic Programming Projection Pursuit for Exploratory Data Modeling
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Icke:2010:WiML,
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author = "Ilknur Icke and Andrew Rosenberg",
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title = "Multi-Objective Genetic Programming Projection Pursuit
for Exploratory Data Modeling",
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booktitle = "Workshop for Women in Machine Learning",
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year = "2010",
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editor = "Diane Oyen",
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address = "Canada",
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month = "6 " # dec,
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keywords = "genetic algorithms, genetic programming, MOG3P",
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URL = "http://arxiv.org/abs/1010.1888",
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URL = "http://pami.uwaterloo.ca/~ealee/wiml/2010/program/WiML2010_IlknurIcke.pdf",
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size = "2 pages",
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abstract = "For classification problems, feature extraction is a
crucial process which aims to find a suitable data
representation that increases the performance of the
machine learning algorithm. According to the curse of
dimensionality theorem, the number of samples needed
for a classification task increases exponentially as
the number of dimensions (variables, features)
increases. On the other hand, it is costly to collect,
store and process data. Moreover, irrelevant and
redundant features might hinder classifier performance.
In exploratory analysis settings, high dimensionality
prevents the users from exploring the data visually.
Feature extraction is a two-step process: feature
construction and feature selection. Feature
construction creates new features based on the original
features and feature selection is the process of
selecting the best features as in filter, wrapper and
embedded methods. In this work, we focus on feature
construction methods that aim to decrease data
dimensionality for visualisation tasks. Various linear
(such as principal components analysis (PCA), multiple
discriminants analysis (MDA), exploratory projection
pursuit) and non-linear (such as multidimensional
scaling (MDS), manifold learning, kernel PCA/LDA,
evolutionary constructive induction) techniques have
been proposed for dimensionality reduction. Our
algorithm is an adaptive feature extraction method
which consists of evolutionary constructive induction
for feature construction and a hybrid filter/wrapper
method for feature selection.",
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notes = "WiML 2010
http://pami.uwaterloo.ca/~ealee/wiml/2010/index.php",
- }
Genetic Programming entries for
Ilknur Icke
Andrew Rosenberg
Citations