MapReduce-based adaptive random forest algorithm for multi-label classification

Research on Control Strategy of Heavy-Haul Train on Long and Steep Downgrades

The control of heavy-haul trains has always been the focus of China’s railway transportation development. One key challenge is the coordination of electric braking and air braking control when the electric-air combined braking is applied on long and steep downgrades. This is normally reliant on manual driving and thus is not cost-effective. To improve the safety and efficiency of train operation in existing heavy-haul railway lines, a multi-label random forest (ML-RF) based approach for heavy-haul train (HHT) operation is proposed. The control characteristics of electric braking and air braking on long and steep downgrades are analyzed first. A prediction model of control strategy is then established with the combination of line conditions and definition of multi-label learning. To evaluate the performance of the model, the 10-fold cross-validation method is adopted. Furthermore, a model parameter optimization algorithm based on evaluation metrics is designed. The feasibility of the proposed approach is demonstrated by the testing results on the actual train running data of one railway line.

Evolutionary Multilabel Classification Algorithm Based on Cultural Algorithm

As one of the common methods to construct classifiers, naïve Bayes has become one of the most popular classification methods because of its solid theoretical basis, strong prior knowledge learning characteristics, unique knowledge expression forms, and high classification accuracy. This classification method has a symmetry phenomenon in the process of data classification. Although the naïve Bayes classifier has high classification performance in single-label classification problems, it is worth studying whether the multilabel classification problem is still valid. In this paper, with the naïve Bayes classifier as the basic research object, in view of the naïve Bayes classification algorithm’s shortage of conditional independence assumptions and label class selection strategies, the characteristics of weighted naïve Bayes is given a better label classifier algorithm framework; the introduction of cultural algorithms to search for and determine the optimal weights is proposed as the weighted naïve Bayes multilabel classification algorithm. Experimental results show that the algorithm proposed in this paper is superior to other algorithms in classification performance.

Application of clustering algorithm in social network user scenario prediction

In order to improve the ability of social network user behavior analysis and scenario pattern prediction, optimize social network construction, combine data mining and behavior analysis methods to perform social network user characteristic analysis and user scenario pattern optimization mining, and discover social network user behavior characteristics. Design multimedia content recommendation algorithms in multimedia social networks based on user behavior patterns. The current existing recommendation systems do not know how much the user likes the currently viewed content before the user scores the content or performs other operations, and the user’s preference may change at any time according to the user’s environment and the user’s identity, Usually in multimedia social networks, users have their own grading habits, or users’ ratings may be casual. Cluster-based algorithm, as an application of cluster analysis, based on clustering, the algorithm can predict the next position of the user. Because the algorithm has a “cold start”, it is suitable for new users without trajectories. You can also make predictions. In addition, the algorithm also considers the user’s feedback information, and constructs a scoring system, which can optimize the results of location prediction through iteration. The simulation results show that the accuracy of social network user scenario prediction using this method is higher, the accuracy of feature registration of social network user scenario mode is improved, and the real-time performance of algorithm processing is better.