Genetic Programming Data Construction Method
(GPDCM) uses in this work to handle one of the key problems in the
supervised learning which is due to the insufficient size of training
dataset. The methodology consists of four stages: first, represent
each record in small dataset as decision tree(DT) where the
collection of these trees represent the population of Genetic
Programming algorithm(GPA). Second, attaching the numerical
value to each node of those trees (Gain information Ratio). These
values represent the fitness of the nodes. Third, expanding the small
population by apply parallel method in three different types of
crossover which is related to the GPA for each pair of the parents.
Fourth, forecasting the classes to new samples generated by
GPDCM using back propagation neural network (BPNN) ,then
apply ROC graphs as a measures of Robustness Evaluation. The
work takes all the important variables in to account, because it is
started by collect DTs and it applies on five different datasets (iris
dataset, weather dataset, heart dataset, soybean dataset and
lamphgraphy dataset). For the theoretical and practical validity, we
compare between the proposed method and the other applied
methods. As the result, we fined that GPDCM is promising
techniques for expanding the extremely small dataset and extracted
a useful knowledge .