Dynamically adaptive genetic algorithm to select training data for SVMs

This paper addresses an important problem of training set selection for support vector machines (SVMs). It is a critical step in case of large and noisy data sets due to high time and memory complexity of the SVM training. There have been several methods proposed so far, in majority underpinned with the analysis of data geometry either in the input or kernel space. Here, we propose a new dynamically adaptive genetic algorithm (DAGA) to select valuable training sets. We demonstrate that not only can DAGA quickly select the training data, but in addition it dynamically determines the desired training set size without any prior information. We analyze the impact of the support vectors ratio, defined as the percentage of support vectors in the training set, on the DAGA performance. Also, we investigate and discuss the possibility of incorporating reduced SVMs into the proposed algorithm. Extensive experimental study shows that DAGA offers fast and effective training set optimization that is independent on the entire training set size.