Variable selection in Poisson regression model using invasive weed optimization algorithm

Variable selection is a very helpful procedure for improving prediction accuracy by finding the most important variables that are related to the response variable. Poisson regression model has received much attention in several science fields for modeling count data. Invasive weed optimization algorithm (IWO) is one of the recently efficient proposed nature-inspired algorithms that can efficiently be employed for variable selection. In this work, IWO algorithm is proposed to perform variable selection for Poisson regression model. Extensive simulation studies and real data application are conducted to evaluate the performance of the proposed method in terms of prediction accuracy and variable selection criteria. The results proved the efficiency of our proposed methods and it outperforms other popular methods.