Constraint handling genetic algorithm for feature engineering in solving classification problems

Constraint handling genetic algorithm for feature engineering in solving classification problems

Автор: Denisov M. A., Sopov E. A.

Журнал: Siberian Aerospace Journal @vestnik-sibsau-en

Рубрика: Informatics, computer technology and management

Статья в выпуске: 1 vol.22, 2021 года.

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Feature engineering in machine learning is a promising but still insufficiently studied direction. Creating new feature space from an original set allows to increase accuracy of the machine learning algorithm chosen to solve complex data mining problems. Some existing selection methods are capable of simultaneously increasing accuracy and reducing feature space. The reduction is an urgent task for big data problems. The paper considers a new machine learning approach for solving classification problems based on feature engineering methods. The design of informative features is carried out using extraction and selection methods. Based on the initial data, new sets of characteristics have been created, which include the original characteristics and characteristics obtained by the method of principal components. The choice of an effective subset of informative features is implemented using a genetic algorithm. In order to avoid overfitting and the creation of trivial classifiers, restrictions are imposed on the fitness function of the genetic algorithm, requiring a certain number of features of the original sample, as well as a certain number of features obtained by the principal component method. A comparative analysis of efficiency of the following classification algorithms is carried out: k-nearest neighbors, support vector machine, and a random forest. Efficiency research experiments are carried out by solving applied binary classification problems from the UCI Machine Learning repository of machine learning problems. The macro F1-score was chosen as an efficiency criterion. The results of numerical experiments show that the proposed approach outperforms the solutions obtained using the original data set and the performance of random feature selection (the low bound for the results). Moreover, the accuracy enhancement is obtained for all types of problems (data sets that have more features than values). All results are proved to be statistically significant.

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Feature selection, feature construction, genetic algorithm, constraint optimization

Короткий адрес: https://sciup.org/148321784

IDR: 148321784   |   DOI: 10.31772/2712-8970-2021-22-1-18-31

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