This paper introduces and develops a novel variable importance score function in the context of ensemble learning, and demonstrates its appeal empirically. Our proposed score function is simple and more straightforward than its counterpart proposed in the context of random forest...
Open access
Research Article10.9734/BJMCS/2016/23945
We propose an information-theoretic alternative to the popular Cronbach alpha coefficient of item reliability. Particularly suitable for contexts in which instruments are scored on a strictly nonnumeric and non ordinal scale, our proposed index is based on functions of the entrop...
Open access
Research Article10.9734/BJMCS/2016/23944
This paper uses stochastic subsampling of the dataset to provide a frequentist approximation to what is known in the Bayesian framework as the posterior inclusion probability (PIP). The distinct merit of this contribution lies in the fact that it makes it easier for typically non...
Open access
Research Article10.9734/BJMCS/2016/27023