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.

Research on Asset Recognition Algorithm of Information Security Product Based on Decision Tree Algorithm

Abstract: In current, there are complex relationship between the assets of information security product According to this characteristic, we propose a new asset recognition algorithm (ART) on the improvement of the C45 decision tree algorithm, and analyze the computational complexity and space complexity of the proposed algorithm Finally, we demonstrate that our algorithm is more precise than C45 algorithm in asset recognition by an application example whose result verifies the availability of our algorithm Keywordsdecision tree, information security product, asset recognition, C45

Decision Tree Classification Model In Water Supply Network

Abstract: With the service life of water supply network (WSN) growth, the growing phenomenon of aging pipe network have become exceedingly serious. Urban water supply network is a hidden underground asset, therefore, it is difficult for monitoring staff to make a direct classification towards the faults of pipe network by means of the modern detecting technology. In this paper, based on the basic property data (e.g. diameter, material, pressure, distance to pump, distance to tank, load, etc.) of water supply network (WSN), decision tree algorithm (C4.5) can be carried out to classify the specific situation of water supply pipeline. Part of the historical data is to establish a decision tree classification model, then the remaining historical data is to validate this established model. Adopting statistical method is to assess the decision tree model including Basic Statistical Method (BSM), Receiver Operating Characteristic (ROC)). These methods can be successfully used to assess the accuracy of this established classification model of water pipe network. The purpose of classification model is to classify the specific condition of water pipe network. It is important to maintain the pipeline according to the classification results including asset unserviceable(AU)、near perfect condition(NPC) and serious deterioration(SD). Finally, this research focuses on pipe classification which plays a significant role in maintaining water supply networks in the future.

A new optimization method for neural tree network model

Abstract: Neural tree network model has been successfully applied to solving a variety of complex nonlinear problems.The optimization of the neural tree model is divided into two steps in general: first structure optimization,and then parameter optimization.One major problem in the evolution of structure without parameter information is noisy fitness evaluation,so an improved breeder genetic programming algorithm is proposed to the synthesis of the optimization in neural tree network model.Simulation results on two time series prediction problems show that the proposed optimization strategy is a potential method with better performance and effectiveness.

Cover tree Bayesian reinforcement learning

Abstract: This paper proposes an online tree-based Bayesian approach for reinforcement learning. For inference, we employ a generalised context tree model. This defines a distribution on multivariate Gaussia...

Inventory Classification and Management Strategy of Components and Parts in Assembly Workshops

Abstract: Material inventory management plays an increasingly important role in modern operations management within manufacturing enterprises. And a multi-attribute classification model has been put up based on the application of the decide tree model and fuzzy artificial neural network. First the material inventory styles are classified. Then a decision tree model is defined based on inventory classification result. The value of the node is decided by Fuzzy Neural Network if multi-attribute decision is needed and material inventory strategy can be decided with the classification tree and inventory strategy table. In the end, the implementation of the model in a manufacturing enterprise resource plan system is presented.