Abstract
Classification is one of the most classic problems in machine learning. Due to the global optimization ability, evolutionary computation (EC) techniques have been successfully applied to solve many problems and the evolutionary classification model is one of the methods used to solve classification problems. Recently, some evolutionary algorithms (EAs) such as the fireworks algorithm (FWA) and brain storm optimization (BSO) algorithm have been employed to implement the evolutionary classification model and achieved the desired results. This means that it is feasible to use EC techniques to solve the classification problem directly. However, the existing evolutionary classification model still has some disadvantages. The limited datasets used in the experiment make the experimental results not convincing enough, and more importantly, the structure of the evolutionary classification model is closely related to the dimension of datasets, which may lead to poor classification performance, especially on large-scale datasets. Therefore, this paper aims at improving the structure of the evolutionary classification model to improve classification performance. Feature selection is an effective method to deal with large datasets, firstly, we introduce the concept of feature selection and use the different feature subsets to construct the structure of the evolutionary classification model. Then, the BSO algorithm is employed to implement the evolutionary classification model to search the optimal structure by search for the optimal feature subset. Moreover, the optimal weight parameters corresponding to the different structures are also searched by the BSO algorithm while searching the optimal feature subset. For verification of the classification effectiveness of the proposed method, 11 different datasets are selected for experiments. The results show that it is feasible to optimize the structure of the evolutionary classification model by introducing feature selection. Moreover, the new method has better classification performance than the original method, especially on large-scale or high-dimensional datasets.
Highlights
Classification problem has been widely studied in machine learning [1]
In the experiment, the performance of the feature selection based on the BSO algorithm (FS-CBSO) method the evolutionary classification model based on the brain storm optimization (BSO) (CBSO) method is compared
After the significant difference test, ‘‘+’’ represents that FS-CBSO results are better than CBSO results, ‘‘-’’ represents that FS-CBSO results are worse than CBSO results, and ‘‘=’’ represents no significant difference between the two algorithms
Summary
Classification problem has been widely studied in machine learning [1]. Many different classification methods have been proposed and been widely used in practical applications. K-nearest neighbor (KNN) [2], naive bayesian. The associate editor coordinating the review of this manuscript and approving it for publication was Gang Li. classification (NBC) [3], support vector machine (SVM) [4], decision tree (DT) [5], artificial neural network (ANN) [6], etc. Classification (NBC) [3], support vector machine (SVM) [4], decision tree (DT) [5], artificial neural network (ANN) [6], etc Many of these methods are structurally deterministic, which makes them often fall into a locally optimal solution.
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