- author = "Ilknur Icke",
- title = "Multi-objective genetic programming for data visualization and classification",
- school = "Computer Science, City University of New York",
- year = "2011",
- address = "USA",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-1-267-01062-9",
-
URL = "
http://files.matlabsite.com/docs/thesis/th930929283.pdf",
- broken = "http://www.gc.cuny.edu/GC-Header/Alumni/Alumni-Dissertations-and-Theses.aspx?page=3&program=Computer+Science&searchterm=genetic%20programming&sortby=author",
- broken = "https://onesearch.cuny.edu/permalink/f/68hr40/CUNY_ALEPH007152073",
-
URL = "
https://www.proquest.com/openview/3dfdfa4e004ad61dabb9f6ce5c3ba68f",
-
URL = "http://dl.acm.org/citation.cfm?id=2395668",
- size = "239 pages",
-
abstract = "The process of knowledge discovery lies on a continuum
ranging between the human driven (manual exploration)
approaches to fully automatic data mining methods. As a
hybrid approach, the emerging field of visual analytics
aims to facilitate human-machine collaborative decision
making by providing automated analysis of data via
interactive visualizations. One area of interest in
visual analytics is to develop data transformation
methods that support visualization and analysis. In
this thesis, we develop an evolutionary computing based
multi-objective dimensionality reduction method for
visual data classification. The algorithm is called
Genetic Programming Projection Pursuit (G3P) where
genetic programming is used in order to automatically
create visualizations of higher dimensional labeled
datasets which are assessed in terms of discriminative
power and interpretability. We consider two forms of
interpretability of the visualizations: clearly
separated and compact class structures along with
easily interpretable data transformation expressions
relating the original data attributes to the
visualization axes. The G3P algorithm incorporates a
number of automated measures of interpretability that
were found to be in alignment with human judgement
through a user study we conducted.
On a number of data mining problems, we show that G3P generates a large number of data transformations that are better than those generated by a number of dimensionality reduction methods such as the principal components analysis (PCA), multiple discriminants analysis (MDA) and targeted projection pursuit (TPP) in terms of discriminative power and interpretability.",
-
notes = "UMI Number: 3481647
Supervisor Andrew Rosenberg",
