Decision tree algorithm is a widely used classification and prediction method. Because it generates a tree-like classifier, it has a simple structure and is extensively used by people. Regardless of the decision tree algorithm, the decision attributes are classified according to the condition attributes. The judgment process is from the root node to the leaf node. Each branch of the tree takes the form of selecting the best split attribute. However, this classification method of decision tree makes it rely too much on training data. If the data are more complicated, there are noisy data, incomplete data, etc. The decision tree will often have overfitting problems. This study mainly analyzes the random forest algorithm model and the CART algorithm and applies the CART algorithm to the model according to the random forest model. Aiming at the algorithm’s shortcomings in solving big data, this study will improve the algorithm through the MapReduce programming model to achieve parallelization of the process and construction of the function. Combining the construction goals and principles of the talent supply chain management system, this study constructs the overall framework and operational process of the enterprise talent supply chain management system based on the decision tree model from the overall level and the operational level. Aiming at the enterprise’s talent management problems, it focuses on designing integrated management, flexible management, talent information integrated management, and evaluation and optimization management models to ensure that the constructed system is operable and measurable and can achieve dynamic optimization. Based on the current situation of talent management in a company, this study analyzes the enterprise talent supply chain management model based on the decision tree model proposed in this study and constructs the overall framework and core model of a company’s talent supply chain management system. The current situation of the company puts forward the safeguard measures for the implementation of the management system to assure that the established management system can be effectively implemented.
It is well known that we, as human beings, are prone to a variety of undesirable emotions such as excitement, boredom, and fear, all of which are induced by varying degrees of negative states. In this paper, we designed an emotion-evoking experiment to induce calm, excited, bored, and fearful emotions, as well as low, moderate, and high levels of tension. Based on the six physiological signals such as heart rate and respiration rate of the subjects in these emotion states, feature extraction was performed after removing the baseline preprocessing, combined with particle swarm optimisation algorithm for feature selection, and the k-nearest neighbour algorithm was used to classify the different emotion and tension levels in the undesirable states. By comparing the results of several sets of experiments, we found that with baseline removal and particle swarm feature selection optimisation, our experimental results using k-nearest neighbour classification showed a significant improvement in recognition accuracy compared to the traditional k-nearest neighbour algorithm, which indicates that the proposed method has better recognition results.
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