Multiclass Object Classification Using Genetic
Programming
M. Zhang, and W. Smart. CS-TR-04-2. Computer Science, Victoria University of Wellington, New Zealand, (2004)
Abstract
genetic programming for multi-class object
classification problems. Rather than using fixed static
thresholds as boundaries to distinguish between
different classes, this approach introduces two methods
of classification where the boundaries between
different classes can be dynamically determined during
the evolutionary process. The two methods are centred
dynamic class boundary determination and slotted
dynamic class boundary determination. The two methods
are tested on four object classification problems of
increasing difficulty and are compared with the
commonly used static class boundary method. The results
suggest that, while the static class boundary method
works well on relatively easy object classification
problems, the two dynamic class boundary determination
methods outperform the static method for more
difficult, multiple class object classification
problems.
%0 Report
%1 vuw-CS-TR-04-2
%A Zhang, Mengjie
%A Smart, Will
%C New Zealand
%D 2004
%K algorithms, boundary class determination, dynamic genetic object programming, recognition
%N CS-TR-04-2
%T Multiclass Object Classification Using Genetic
Programming
%U http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-04/CS-TR-04-2.pdf
%X genetic programming for multi-class object
classification problems. Rather than using fixed static
thresholds as boundaries to distinguish between
different classes, this approach introduces two methods
of classification where the boundaries between
different classes can be dynamically determined during
the evolutionary process. The two methods are centred
dynamic class boundary determination and slotted
dynamic class boundary determination. The two methods
are tested on four object classification problems of
increasing difficulty and are compared with the
commonly used static class boundary method. The results
suggest that, while the static class boundary method
works well on relatively easy object classification
problems, the two dynamic class boundary determination
methods outperform the static method for more
difficult, multiple class object classification
problems.
@techreport{vuw-CS-TR-04-2,
abstract = {genetic programming for multi-class object
classification problems. Rather than using fixed static
thresholds as boundaries to distinguish between
different classes, this approach introduces two methods
of classification where the boundaries between
different classes can be dynamically determined during
the evolutionary process. The two methods are centred
dynamic class boundary determination and slotted
dynamic class boundary determination. The two methods
are tested on four object classification problems of
increasing difficulty and are compared with the
commonly used static class boundary method. The results
suggest that, while the static class boundary method
works well on relatively easy object classification
problems, the two dynamic class boundary determination
methods outperform the static method for more
difficult, multiple class object classification
problems.},
added-at = {2008-06-19T17:46:40.000+0200},
address = {New Zealand},
author = {Zhang, Mengjie and Smart, Will},
biburl = {https://www.bibsonomy.org/bibtex/23b76e7fd941c5bc4640117f3aa71331a/brazovayeye},
institution = {Computer Science, Victoria University of Wellington},
interhash = {d3248f3bf00c8b3e70061fa61782ce1b},
intrahash = {3b76e7fd941c5bc4640117f3aa71331a},
keywords = {algorithms, boundary class determination, dynamic genetic object programming, recognition},
number = {CS-TR-04-2},
size = {15 pages},
timestamp = {2008-06-19T17:55:33.000+0200},
title = {Multiclass Object Classification Using Genetic
Programming},
url = {http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-04/CS-TR-04-2.pdf},
year = 2004
}