Feature selection problem is one of the most significant issues in data classification. The purpose of feature selection is selection of the least number of features in order to increase accuracy and decrease the cost of data classification. In recent years, due to appearance of high-dimensional datasets with low number of samples, classification models have encountered over-fitting problem. Therefore, the need for feature selection methods that are used to remove the extensions and irrelevant features is felt. Recently, although, various methods have been proposed for selecting the optimal subset of features with high precision, these methods have encountered some problems such as instability, high convergence time, selection of a semi-optimal solution as the final result. In other words, they have not been able to fully extract the effective features. In this paper, a hybrid method based on the IWSSr method and Shuffled Frog Leaping Algorithm (SFLA) is proposed to select effective features in a large-scale gene dataset. The proposed algorithm is implemented in two phases: filtering and wrapping. In the filter phase, the Relief method is used for weighting features. Then, in the wrapping phase, by using the SFLA and the IWSSr algorithms, the search for effective features in a feature-rich area is performed. The proposed method is evaluated by using some standard gene expression datasets. The experimental results approve that the proposed approach in comparison to similar methods, has been achieved a more compact set of features along with high accuracy. The source code and testing datasets are available at https://github.com/jimy2020/SFLA_IWSSr-Feature-Selection.

In this approach, flower-like cobalt with a hierarchical structure was applied as modifier for voltammetric determination of chlorpheniramine, which is an antihistaminic drug. The flower-like cobalt nanostructures were synthesized by using a simple chemical method. They have been characterized by using scanning electron microscopy and cyclic voltammetry. The carbon paste electrode modified with cobalt nanostructures shows an excellent electrocatalytic activity and sensitivity toward chlorpheniramine due to its unique properties such as high specific surface area and large pore volume. Potential sweep rate and pH effects on the response of the electrode for the oxidation of chlorpheniramine were investigated. Differential pulse voltammetry has been applied for quantitative determination of chlorpheniramine. A dynamic linear range was obtained in the range of 1.0 × 10 −7 −1.0 × 10 −5 mol L −1 , and the detection limit was estimated to be 8.0 × 10 −8 mol L −1 .