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Since the number of features of the dataset is much higher than the number of patterns, the higher the dimension of the data, the greater the impact on the learning algorithm. Dimension disaster has become an important problem. Feature selection can effectively reduce the dimension of the dataset and improve the performance of the algorithm. Thus, in this paper, A feature selection algorithm based on P systems (P-FS) is proposed to exploit the parallel ability of cell-like P systems and the advantage of evolutionary algorithms in search space to select features and remove redundant information in the data. The proposed P-FS algorithm is tested on five UCI datasets and an edible oil dataset from practical applications. At the same time, the P-FS algorithm and genetic algorithm feature selection (GAFS) are compared and tested on six datasets. The experimental results show that the P-FS algorithm has good performance in classification accuracy, stability, and convergence. Thus, the P-FS algorithm is feasible in feature selection. [ABSTRACT FROM AUTHOR]

Feature selection is one of the hottest machine learning topics in recent years. The main purposes of it are to simplify the original model, improve the readability of the model, and prevent over-fitting by searching for a suitable subset of features. There are many methods for this problem, including evolutionary algorithms and particle swarm optimization. Among them, the competitive swarm optimizer is a new optimization algorithm proposed in recent years, which is based on particle swarm optimization algorithm, and has achieved good results in high-dimensional feature selection problems, but it also has the problems of high computation time cost and easily being premature. Aiming at these problems, this paper proposes to add the crossover operator and mutation operator in the genetic algorithm to the competitive swarm optimization, so as to improve the generation speed of new individuals in the algorithm and prevent premature population. After testing on UC Irvine Machine Learning Repository, the new algorithm not only improves the computational efficiency, but also avoids the problem that the competitive swarm optimization algorithm is easy to fall into the local optimum, which greatly improves the calculation effect. [ABSTRACT FROM AUTHOR]

In this paper, we develop a novel feature selection algorithm based on the genetic algorithm (GA) using a specifically devised trace-based separability criterion. According to the scores of class separability and variable separability, this criterion measures the significance of feature subset, independent of any specific classification. In addition, a mutual information matrix between variables is used as features for classification, and no prior knowledge about the cardinality of feature subset is required. Experiments are performed by using a standard lung cancer dataset. The obtained solutions are verified with three different classifiers, including the support vector machine (SVM), the back-propagation neural network (BPNN), and the K-nearest neighbor (KNN), and compared with those obtained by the whole feature set, the F-score and the correlation-based feature selection methods. The comparison results show that the proposed intelligent system has a good diagnosis performance and can be used as a promising tool for lung cancer diagnosis. [ABSTRACT FROM AUTHOR]