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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.
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TL;DR: The hybrid tree model in HiTrust can embed both policies and credential sets in a tree node, and is able to describe fine-grained security policy with attributes or negotiation context information, which endows the HiTrust with the capability of describing complex trust establishment requirements.
Abstract: In a pervasive computing environment, the need to establish trust amongst distributed services has attracted increasing attentions from both the industry and academia. As a widely adopted solution to carry a principal's identity and attributes of different organizations, the credential-based trust establishment has become popular over Internet. In this paper, we propose a hybrid negotiation tree based modeling approach, named HiTrust, to build cross-organizational trust relationship. The HiTrust is used to characterize the gradual interactions state during the trust establishment between the principals from different security organizations. Compared with the original disclosure tree model, the hybrid tree model in HiTrust can embed both policies and credential sets in a tree node, and is able to describe fine-grained security policy with attributes or negotiation context information. This property endows the HiTrust with the capability of describing complex trust establishment requirements, and makes it more efficient to search desired tree node. Furthermore, to enhance the usability and efficiency of negotiation service, we propose a session state maintenance mechanism based on a policy stack and an asynchronous trust chain propagation mechanism. We have implemented the HiTrust prototype system, and experimentally verified that the HiTrust is effective and scalable.
2 citations
TL;DR: A novel decision tree packet classification algorithm based on Efficient Multiple Bit Selection (EMBS), where prefix fields are transformed to a set of independent bits; and multiple arbitrary bits are selected to cut nodes when building the decision tree.
2 citations
Journal Article•
TL;DR: The results indicate that the measurement model and computation method of data complexity are useful to measure the complexity of information system, a prerequisite for information systems cost estimating and information systems pricing.
2 citations
TL;DR: Xu et al. as mentioned in this paper proposed a physics-based algorithm to simulate tree dynamics under wind load, and the algorithm was integrated with a landscape model in which different types of trees were constructed with an L-system-based formalism.
Abstract: Rapid development in the different computer science fields during the recent decades has facilitated the creation of new applications in the area of dynamic simulation of plant development. Among these new applications, simulation of trees swaying in the wind is of great importance, as those computer graphics related areas, e.g., computer games, tree cultivation and forest management simulations, help a lot in revealing the mechanisms of tree dynamics under wind load. However, it is a big challenge to balance the effect of visualization in real time and calculation efficiency for any simulation algorithm. A physics-based algorithm to simulate tree dynamics under wind load was proposed in this study. A mechanistic model simulating the bending of a cantilever beam was used within the algorithm to simulate deformation of stems, and the algorithm was integrated with a landscape model in which different types of trees were constructed with an L-system-based formalism. Simulation results show that realistic dynamic effects can be achieved with reasonably high computational efficiency.
Keywords: physics-based algorithm, tree model, L-system, Cantilever beam
DOI: 10.25165/j.ijabe.20201302.4967
Citation: Xu L F, Yang Z Z, Ding W L, Buck-Sorlin G. Physics-based algorithm to simulate tree dynamics under wind load. Int J Agric & Biol Eng, 2020; 13(2): 26–32.
2 citations
06 Sep 2011
TL;DR: The results show that the proposed method can not only decreases the workload of feature datum extraction, but also identifies the fault patterns rapidly and accurately, and it exhibits better engineering practicality comparing with the C4.5-based method.
Abstract: With rough set theory for knowledge reduction capability and C4.5 decision tree algorithm for fast classification of strengths, an improved rough set-decision tree model for fault diagnosis of wind generation system is built. The results show that the proposed method can not only decreases the workload of feature datum extraction, but also identifies the fault patterns rapidly and accurately, and it exhibits better engineering practicality comparing with the C4.5-based method.
2 citations