6,616 research outputs found
Recommended from our members
The effect of missing values using genetic programming on evolvable diagnosis
Medical databases usually contain missing values due the policy of
reducing stress and harm to the patient. In practice missing values has been a
problem mainly due to the necessity to evaluate mathematical equations obtained
by genetic programming. The solution to this problem is to use fill in methods to
estimate the missing values. This paper analyses three fill in methods: (1) attribute
means, (2) conditional means, and (3) random number generation. The methods
are evaluated using sensitivity, specificity, and entropy to explain the exchange in
knowledge of the results. The results are illustrated based on the breast cancer
database. Conditional means produced the best fill in experimental results
Genetic Programming is Naturally Suited to Evolve Bagging Ensembles
Learning ensembles by bagging can substantially improve the generalization
performance of low-bias, high-variance estimators, including those evolved by
Genetic Programming (GP). To be efficient, modern GP algorithms for evolving
(bagging) ensembles typically rely on several (often inter-connected)
mechanisms and respective hyper-parameters, ultimately compromising ease of
use. In this paper, we provide experimental evidence that such complexity might
not be warranted. We show that minor changes to fitness evaluation and
selection are sufficient to make a simple and otherwise-traditional GP
algorithm evolve ensembles efficiently. The key to our proposal is to exploit
the way bagging works to compute, for each individual in the population,
multiple fitness values (instead of one) at a cost that is only marginally
higher than the one of a normal fitness evaluation. Experimental comparisons on
classification and regression tasks taken and reproduced from prior studies
show that our algorithm fares very well against state-of-the-art ensemble and
non-ensemble GP algorithms. We further provide insights into the proposed
approach by (i) scaling the ensemble size, (ii) ablating the changes to
selection, (iii) observing the evolvability induced by traditional subtree
variation. Code: https://github.com/marcovirgolin/2SEGP.Comment: Added interquartile range in tables 1, 2, and 3; improved Fig. 3 and
its analysis, improved experiment design of section 7.
- …