摘要：Classification performance is adversely impacted by noisy data .Selecting features relevant to the problem is thus a critical step in classification and difficult to achieve accurate solution, especially when applied to a large data set. In this article, we propose a novel filter-based floating search technique for feature selection to select an optimal set of features for classification purposes. A genetic algorithm is utilized to increase the quality of features selected at each iteration. A criterion function is applied to choose relevant and high-quality features which can improve classification accuracy. The method is evaluated using 20 standard machine learning datasets of various sizes and complexities. Experimental results with the datasets show that the proposed method is effective and performs well in comparison with previously reported techniques.
