We "sampled" the fluxes using a fixed interval (1 32 days) or a rule-based (decision tree-based) sampling method.
To judge the sensitivity of our rule-based method to reductions in sample size, we computed the relative amount of climate space that would have been detected by our method had our sample been a random subset of the original 149 localities [2].
Assume that for a given training set of samples, a rule-based classifier (or a decision tree), has been constructed.
We have implemented the platform based on rule-based modelling.
In this paper, we present Logic Alignment Free (LAF), a method that combines alignment-free techniques and rule-based classification algorithms in order to assign biological samples to their taxa.
Glaab, E., Bacardit, J., Garibaldi, J. M. & Krasnogor, N. Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data.
A rule-based method was developed to automatically identify a reliable sample of winter wheat field time series, and to infer the corresponding sowing dates.
We apply LAF to the previously described filtered data set of bacterial genomes, setting k∈ [ 3…6] and using the four already mentioned rule-based classification algorithms by adopting a 10-fold cross validation sampling scheme.
To answer this need, we introduce a rule-based haplotype comparison method, Haplous, which identifies haplotypes within multiple samples from phased genotype data and compares them within and between sample groups.
As the phase I study was primarily a descriptive analysis of safety and tolerability, no formal sample size estimation was performed, with a rule-based design being used (Simon, 2008).
We estimated the effect of sample size on the Rodda et al. rule-based algorithm for climate matching (Fig. 11), in which about 55% of the climate space was detected in an average sample of 10% of our localities.
