TY - GEN
T1 - Exploring boundaries
T2 - 14th International Conference on Genetic and Evolutionary Computation, GECCO'12
AU - Fitzgerald, Jeannie
AU - Ryan, Conor
PY - 2012
Y1 - 2012
N2 - This paper explores a range of class boundary determination techniques that can be used to improve performance of Genetic Programming (GP) on binary classification tasks. These techniques involve selecting an individualised boundary threshold in order to reduce implicit bias that may be introduced through employing arbitrarily chosen values. Individuals that can chose their own boundaries and the manner in which they are applied, are freed from having to learn to force their outputs into a particular range or polarity and can instead concentrate their efforts on seeking a problem solution. Our investigation suggests that while a particular boundary selection method may deliver better performance for a given problem, no single method performs best on all problems studied. We propose a new flexible combined technique which gives near optimal performance across each of the tasks undertaken. This method together with seven other techniques is tested on six benchmark binary classification data sets. Experimental results obtained suggest that the strategy can improve test fitness, produce smaller less complex individuals and reduce run times. Our approach is shown to deliver superior results when benchmarked against a standard GP system, and is very competitive when compared with a range of other machine learning algorithms.
AB - This paper explores a range of class boundary determination techniques that can be used to improve performance of Genetic Programming (GP) on binary classification tasks. These techniques involve selecting an individualised boundary threshold in order to reduce implicit bias that may be introduced through employing arbitrarily chosen values. Individuals that can chose their own boundaries and the manner in which they are applied, are freed from having to learn to force their outputs into a particular range or polarity and can instead concentrate their efforts on seeking a problem solution. Our investigation suggests that while a particular boundary selection method may deliver better performance for a given problem, no single method performs best on all problems studied. We propose a new flexible combined technique which gives near optimal performance across each of the tasks undertaken. This method together with seven other techniques is tested on six benchmark binary classification data sets. Experimental results obtained suggest that the strategy can improve test fitness, produce smaller less complex individuals and reduce run times. Our approach is shown to deliver superior results when benchmarked against a standard GP system, and is very competitive when compared with a range of other machine learning algorithms.
KW - binary classification
KW - genetic programming
KW - medical
UR - http://www.scopus.com/inward/record.url?scp=84864703851&partnerID=8YFLogxK
U2 - 10.1145/2330163.2330267
DO - 10.1145/2330163.2330267
M3 - Conference contribution
AN - SCOPUS:84864703851
SN - 9781450311779
T3 - GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation
SP - 743
EP - 750
BT - GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation
Y2 - 7 July 2012 through 11 July 2012
ER -