Text feature selection method based on invasive weed optimization

Abstract

In order to select text feature more comprehensively and improve the accuracy of the text feature selection,a new text feature selection method based on Invasive Weed Optimization(IWO) was proposed.The biggest advantage of IWO is that the offspring individuals are being randomly spread around their parents according to Gauss normal distribution,and the standard deviation of the random function is adjusted dynamically during the evolution process;thus,the algorithm explores new areas aggressively to maintain the diversity of the species in the early and middle iterations,and enhances the feature selection of the optimal individuals in final iteration.Such mechanism ensured the steady convergence of the algorithm to global optimal solution,and improved the accuracy of the text feature selection.The results of experiments indicate that this method can provide the entry of low weight value with feature selection opportunity,and ensure the feature selection advantage of the entry with high weight value,thereby enhancing the completeness and accuracy of the text feature selection.