Comparison of the computational complexity of learning a single model and three methods for learning ensembles.

<p>A graphical representation of the time complexity of learning a (A) single process-based model and ensembles of process-based models with five constituents with (B) bagging, (C) boosting and (D) library sampling, using the library presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153507#pone.0153507.t001" target="_blank">Table 1</a>.</p

Library of domain knowledge for modeling Predator-Prey dynamics.

<p>Library of domain knowledge for modeling Predator-Prey dynamics.</p

Comparison of the average ranks for predictive performance of the two library sampling methods to two alternative ensemble approaches.

<p>Average ranks of four different types of ensembles with 10 constituents combined by averaging. The constituents are: constructed via library sampling with and without duplicates, the 10 best models learned from the complete library, and 10 random models learned from the complete library. The average ranks of predictive performance are computed over the 15 data sets.</p

Comparison of the predictive performance of a single model with that of ensembles of PBMs learned with library sampling, bagging and boosting.

<p>Comparison of the predictive performance of a single model with that of ensembles of PBMs learned with library sampling, bagging and boosting.</p

Comparison of the average ranks for predictive performance of the two library sampling methods with different ensemble sizes.

<p>Average ranks of ensembles that include 10, 25, and 50 base models in terms of predictive performance averaged over the 15 experimental data sets for Library sampling (A) with and (B) without duplicates.</p

Comparison of the predictive performance in terms of average ranks of the two library sampling methods with different methods for choosing ensemble constituents.

<p>Average ranks of ensembles with 10, 25 and 50 constituents combined by averaging and selected differently (based on their train (subscript T) or validation (subscript V) performance). The average ranks refer to the predictive (testing) model performance averaged over the 15 experimental data sets, separately for the case of Library sampling (A) with and (B) without duplicates.</p

Comparison of the average ranks for descriptive performance (on training data) of the two library sampling methods with different methods for choosing ensemble constituents.

<p>Average ranks of ensembles with 10, 25 and 50 constituents combined by averaging and selected differently (based on their train (subscript T) or validation (subscript V) performance). The average ranks refer to the descriptive model (training) performance averaged over the 15 experimental data sets, separately for the case of Library sampling (A) with and (B) without duplicates.</p

Comparison of the average ranks for predictive performance of the library sampling method to learning single models, bagging and boosting.

<p>Average ranks of the ensembles constructed by library sampling with 10 constituents combined by averaging to the performance of: a single model and two types of ensembles combined by averaging constructed by bagging and boosting (with 25 constituents). The ranks of the models in terms of their predictive performance and the average CPU times for learning them are averaged over the 15 data sets.</p

Comparison of the average ranks for predictive performance of the two library sampling methods with four combining methods.

<p>Average ranks of the four methods for combining the simulations of base models (average, weighted average, median and weighted median) in terms of predictive performance averaged over the 15 experimental data sets for Library sampling (A) with and (B) without duplicates.</p

A simple Predator-Prey model and its process-based modeling representation.

<p>(A) A graphical representation of the entities (white boxes) and processes (black arrows) in a simple Predator-Prey model and (B) its process-based modeling representation.</p