Decision tree model

About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.

An efficient algorithm for virtual network function placement and chaining

Abstract: The virtualized network functions placement and chaining problem is formulated as a decision tree to reduce significantly the complexity of service function chaining (SFC) in clouds. Each node in the tree corresponds to a virtual resource embedding and each tree branch to the mapping of a client request in some physical candidate. This transforms the placement problem to a decision tree search. We devise a new algorithm based on the Monte Carlo Tree Search (MCTS) to incrementally build and search within the decision tree. Thanks to the proposed SFC-MTCS strategy, an optimized solution is computed in a reasonable time. Extensive simulations assess the performance and show that SFC-MCTS outperforms state of the art strategies in terms of: i) acceptance rate, ii) providers revenue and iii) execution time.

Analysis and Study of Diabetes Follow-Up Data Using a Data-Mining-Based Approach in New Urban Area of Urumqi, Xinjiang, China, 2016-2017.

Abstract: The focus of this study is the use of machine learning methods that combine feature selection and imbalanced process (SMOTE algorithm) to classify and predict diabetes follow-up control satisfaction data. After the feature selection and unbalanced process, diabetes follow-up data of the New Urban Area of Urumqi, Xinjiang, was used as input variables of support vector machine (SVM), decision tree, and integrated learning model (Adaboost and Bagging) for modeling and prediction. The experimental results show that Adaboost algorithm produces better classification results. For the test set, the G-mean was 94.65%, the area under the ROC curve (AUC) was 0.9817, and the important variables in the classification process, fasting blood glucose, age, and BMI were given. The performance of the decision tree model in the test set is relatively lower than that of the support vector machine and the ensemble learning model. The prediction results of these classification models are sufficient. Compared with a single classifier, ensemble learning algorithms show different degrees of increase in classification accuracy. The Adaboost algorithm can be used for the prediction of diabetes follow-up and control satisfaction data.

A destination-driven shortest path tree algorithm

Abstract: Shortest path tree (SPT) is the most widely-used multicast tree type due to its simplicity and low per-destination cost. An SPT is constructed by the union of the shortest paths from the source node to each destination. However, SPT does not consider overall network resource utilization. We propose a destination-driven shortest path tree algorithm, which aims to construct a low-cost SPT by considering link sharing between different destinations. The computational complexity of the presented algorithm is O(|E|log|V|), where |E| and |V| are the number of edges and nodes in a network respectively. Simulation results are used to demonstrate the high performance of the proposed algorithm.

Lossless Compression of Color Map Images by Context Tree Modeling

Abstract: Significant lossless compression results of color map images have been obtained by dividing the color maps into layers and by compressing the binary layers separately using an optimized context tree model that exploits interlayer dependencies. Even though the use of a binary alphabet simplifies the context tree construction and exploits spatial dependencies efficiently, it is expected that an equivalent or better result would be obtained by operating directly on the color image without layer separation. In this paper, we extend the previous context-tree-based method to operate on color values instead of binary layers. We first generate an n-ary context tree by constructing a complete tree up to a predefined depth, and then prune out nodes that do not provide compression improvements. Experiments show that the proposed method outperforms existing methods for a large set of different color map images

Personalized shoe tree mold designing method

Abstract: The present invention discloses method of designing shoe tree model with specific individuality, in said method, 3D scanner is utilized to obtain 3D data of foot and then makes model according to theobtained data, then compare the model with standard shoe tree model and regulates the model to form specific shoe tree model which fits the foot shape of specified customer. The shoe made with the invented shoe tree possesses individuality and is comfort to wear.