Random forest feature selection performed over varying training sets provides a subset of generalized CIEL*a*b* co-occurrence texture features, while sample selection strategies with minimal constraints reduce training data requirements to achieve reliable results.
(A sample selection is here).
As a result of these constraints, many of the basic principles of impact evaluation design (comparable pretest posttest design, control group, instrument development and testing, random sample selection, control for researcher bias, thorough documentation of the evaluation methodology, etc).
The sample selection described in Sect.
Various factors were considered in sample selection.
Fig. 1 Flow diagram of sample selection.
Weighted for sample selection and attrition.
Selection term Term constructed from the selection model that controls for sample selection bias.
In addition, two exponential models, namely, with sample selection, without sample selection, were developed.
