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In the ensemble feature selection method, if the weight adjustment is performed on each feature subset used, the ensemble effect can be significantly different; therefore, how to find the optimized weight vector is a key and challenging problem. Aiming at this optimization problem, this paper proposes an ensemble feature selection approach based on genetic algorithm (EFS-BGA). After each base feature selector generates a feature subset, the EFS-BGA method obtains the optimized weight of each feature subset through genetic algorithm, which is different from traditional genetic algorithm directly processing single features. We divide the EFS-BGA algorithm into two types. The first is a complete ensemble feature selection method; based on the first, we further propose the selective EFS-BGA model. After that, through mathematical analysis, we theoretically explain why weight adjustment is an optimization problem and how to optimize. Finally, through the comparative experiments on multiple data sets, the advantages of the EFS-BGA algorithm in this paper over the previous ensemble feature selection algorithms are explained in practice. [ABSTRACT FROM AUTHOR]

Heart disease is the most life-threatening disease globally, affecting human life very critically. On-time and precise diagnosis of heart disease is vital for the prevention and treatment of heart failure. In the earlier stage, several detection techniques were developed to detect heart disease using machine learning techniques. But the heart problems have not been detected accurately with the minimum required time. This paper presented an effective heart disease prediction system that accurately diagnoses abnormality within a short duration. The feature selection process is carried with stochastic gradient boosting with a recursive feature elimination approach. For classification, the features are clustered by using the adaptive Harris hawk optimization clustering approach. Based on clustered features, the classification is processed with an enhanced deep genetic algorithm. This method improves the performance of the deep neural network by augmenting its initial weights with an enhanced genetic algorithm, which recommends better weights for the neural network. The demonstration of the proposed work is simulated in the MATLAB platform and the dataset used in UCI machine learning repository. The experimental outcome shows that the developed feature selection and classification approach attain 98.36% and 97.3% accuracy compared with the state-of-the-art techniques. [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]