scispace - formally typeset
Search or ask a question
Topic

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.


Papers
More filters
Proceedings ArticleDOI
25 Jun 2019
TL;DR: This work proposes a sound, general framework for multi-parameter analysis of security, which relies on the attack– defense tree model that security experts from industry are already familiar with, and presents mathematical foundations of the framework and characterize the class of parameters it is suitable for.
Abstract: The cheapest attacks are often time-consuming, and those requiring high level of technical skills might occur rarely but result in disastrous consequences. Therefore, analysis focusing on a single parameter at a time, e.g., only cost or time, is insufficient for the successful selection of the appropriate measures increasing system^{\prime}s security. In practice, security engineers are thus confronted with the problem of multi-parameter analysis. The objective of this work is to address this problem and propose a sound, general framework for multi-parameter analysis of security. In order to ensure the usability of our solution for real-life applications, our proposal relies on the attack– defense tree model that security experts from industry are already familiar with. We present mathematical foundations of our framework and characterize the class of parameters it is suitable for. We identify conditions under which the proposed method applies to attack–defense trees where several nodes represent the same action. We discuss the complexity of our approach and implement the underlying algorithms in a proof of concept tool. We analyze its performance on a number of trees of varying complexity, and validate our proposal on a case study borrowed from industry.

15 citations

Journal ArticleDOI
TL;DR: The resource tree model and a new separation logic that extends the Bunched Implications logic with a modality for locations are defined and it is shown how the model and its associated language can be used to manage heap structures and also permission accounting.
Abstract: In this article, we propose a new data structure, called resource tree, that is a node-labelled tree in which nodes contain resources which belong to a partial monoid. We define the resource tree model and a new separation logic (BI-Loc) that extends the Bunched Implications logic (BI) with a modality for locations. In addition, we consider quantifications on locations and paths and then we study decidability by model-checking in these models and logics. Moreover, we define a language to deal with resource trees and also an assertion logic derived from BI-Loc. Then soundness and completeness issues are studied, and we show how the model and its associated language can be used to manage heap structures and also permission accounting.

15 citations

Proceedings Article
Mikhail Ju. Moshov1, Igor Chikalov1
01 Apr 2004
TL;DR: Algorithm which allow to optimize decision trees consecutively againsts relatively different criterions for decision tables over an arbitrary infinite restricted information system have polynomial time complexity are considered.
Abstract: In the paper algorithms are considered which allow to optimize decision trees consecutively againsts relatively different criterions. For decision tables over an arbitrary infinite restricted information system [4], these algorithms have polynomial time complexity.

15 citations

Journal ArticleDOI
TL;DR: This work presents an algorithm that performs a point location query with O(d^2\log n) linear comparisons, improving the previous best result by about a factor of d and has currently the best performance for arbitrary hyperplanes.
Abstract: We consider the point location problem in an arrangement of n arbitrary hyperplanes in any dimension d, in the linear decision tree model, in which we only count linear comparisons involving the query point, and all other operations do not explicitly access the query and are for free. We mainly consider the simpler variant (which arises in many applications) where we only want to determine whether the query point lies on some input hyperplane. We present an algorithm that performs a point location query with $$O(d^2\log n)$$ linear comparisons, improving the previous best result by about a factor of d. Our approach is a variant of Meiser’s technique for point location (Inf Comput 106(2):286–303, 1993) (see also Cardinal et al. in: Proceedings of the 24th European symposium on algorithms, 2016), and its improved performance is due to the use of vertical decompositions in an arrangement of hyperplanes in high dimensions, rather than bottom-vertex triangulation used in the earlier approaches. The properties of such a decomposition, both combinatorial and algorithmic (in the standard real RAM model), are developed in a companion paper (Ezra et al. arXiv:1712.02913 , 2017), and are adapted here (in simplified form) for the linear decision tree model. Several applications of our algorithm are presented, such as the k-SUM problem and the Knapsack and SubsetSum problems. However, these applications have been superseded by the more recent result of Kane et al. (in: Proceedings of the 50th ACM symposium on theory of computing, 2018), obtained after the original submission (and acceptance) of the conference version of our paper (Ezra and Sharir in: Proceedings of the 33rd international symposium on computational geometry, 2017). This result only applies to ‘low-complexity’ hyperplanes (for which the $$\ell _1$$ -norm of their coefficient vector is a small integer), which arise in the aforementioned applications. Still, our algorithm has currently the best performance for arbitrary hyperplanes.

15 citations

Journal ArticleDOI
TL;DR: The problem of determining the asymptotic time complexity of the convex matrix searching problem is equivalent to determining the minimum decision tree height.

14 citations


Network Information
Related Topics (5)
Cluster analysis
146.5K papers, 2.9M citations
80% related
Artificial neural network
207K papers, 4.5M citations
78% related
Fuzzy logic
151.2K papers, 2.3M citations
77% related
The Internet
213.2K papers, 3.8M citations
77% related
Deep learning
79.8K papers, 2.1M citations
77% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202310
202224
2021101
2020163
2019158
2018121