Improving Floating Search Feature Selection using Genetic Algorithm

Classification, a process for predicting the class of a given input data, is one of the most fundamental tasks in data mining. Classification performance is negatively affected by noisy data and therefore selecting features relevant to the problem is a critical step in classification, especially when applied to large datasets. In this article, a novel filter-based floating search technique for feature selection to select an optimal set of features for classification purposes is proposed. A genetic algorithm is employed to improve the quality of the features selected by the floating search method in each iteration. A criterion function is applied to select relevant and high-quality features that can improve classification accuracy. The proposed method was evaluated using 20 standard machine learning datasets of various size and complexity. The results show that the proposed method is effective in general across different classifiers and performs well in comparison with recently reported techniques. In addition, the application of the proposed method with support vector machine provides the best performance among the classifiers studied and outperformed previous researches with the majority of data sets

Modified Floating Search Feature Selection Based on Genetic Algorithm

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