An Improved Invasive Weed Algorithm with RBFNN for Optimizing Classification Problems

Abstract

Classification or decision-making task is very common in every field of endeavor. Many algorithms have been developed to help to make this kind of task successful. Invasive Weed Optimization (IWO) has been commonly used to help perform this task. However, its classification accuracy still leaves something to be desired. This study attempted to develop an improved Invasive Weed Optimization (IIWO) that would perform this task better. Our objectives were as follows: 1) to use IWO to optimize the parameters of Radial Basis Function Neural Network classifier and 2) to improve the dispersal of offspring solutions in the search space by using 3 spatial distributions: Normal, Cauchy, and Levy distributions, instead of only one as in IWO. We evaluated the performance of IIWO against two conventional classification algorithms: Genetic Algorithm (GA) and Gradient Descent (GD) algorithm on 5 benchmark datasets and found that IIWO made more accurate predictions than these two algorithms on 3 out of the 5 datasets and nearly as accurate as them on the other 2 datasets. The reasons that IIWO performed as well as this was that the inner working of RBFNN allowed the algorithm to estimate the objective value more accurately and the use of 3 spatial distributions to disperse offspring solutions instead of one in IWO helped make the offspring solutions spread to cover the whole search space better.