Abstract. In recent years, machine learning (ML) modeling (often referred to as artificial intelligence) has become increasingly popular for personnel selection purposes. Numerous organizations use ML-based procedures screening large candidate pools, while some companies try automate the hiring process far possible. Since ML models can handle sets of predictor variables and are therefore able i...

Abstract With the development of artificial intelligence technology, machine learning has achieved very good results in field stock selection. This paper mainly studies application linear model, clustering, support vector machine, random forest, neural network and deep methods The main contribution this is to provide a new idea for traditional quantitative investors, so that they can build more...

In order to automatically evaluate the welding quality during high-power disk laser welding, a real-time monitoring system was developed. The images of laser-induced metal vapor during welding were captured and fifteen features were extracted. A feature selection method based on a sequential forward floating selection algorithm was employed to identify the optimal feature subset, and a support ...

In machine learning and pattern recognition, feature selection has been a hot topic in the literature. Unsupervised feature selection is challenging due to the loss of labels which would supply the related information.How to define an appropriate metric is the key for feature selection. We propose a filter method for unsupervised feature selection which is based on the “Confidence Machine”. Con...

Original scientific paper In a manufacturing system, inappropriate machine selection may lead to many problems by negatively affecting productivity, precision, flexibility and product quality, and machine selection is considered to be an important subject to make the system effective. A Multi-Criteria Decision Making (MCDM) which is relying on the different criteria and alternatives is to choos...

When machine learning is applied in safety-critical or otherwise sensitive areas, the analysis of feature relevance can be an important tool to keep the size of models small, and thus easier to understand, and to analyze how different features impact the behavior of the model. In the presence of correlated features, feature relevances and the solution to the minimal-optimal feature selection pr...