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Time and Space Complexity of Deterministic and Nondeterministic Decision Trees: Local Approach
2023 IEEE International Conference on Big Data (BigData), 2023Mikhail Moshkov, Kerven Durdymyradov
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Decision Support Using Deterministic Equivalents of Probabilistic Game Trees
2012 IEEE 19th International Conference and Workshops on Engineering of Computer-Based Systems, 2012We have developed a game-theory driven decision-support tool that builds probabilistic game trees automatically from user-defined actions, rules, and states. The result of evaluating the paths in the game tree is a series of decisions which forms a decision-path representing an epsilon-Nash-Equilibrium.
Jerzy W. Rozenblit
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Deterministic and Nondeterministic Decision Trees for Rough Computing
Fundamenta Informaticae, 2000In the paper, infinite information systems are considered which are used in pattern recognition, discrete optimization, computational geometry. Depth and size of deterministic and nondeterministic decision trees over such information systems are studied. Two classes of infinite information systems are investigated.
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Lower Bounds on Complexity of Deterministic Decision Trees for Decision Tables
2020In this chapter, lower bounds on complexity of deterministic decision trees for decision tables are studied that are based on the notions of super-cover, super-partition, test, and system of representatives for decision tables. An approach to the proof of lower bounds is also considered which is based on the use of so-called proof-trees.
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COMPARITIVE ANALYSIS OF DETERMINISTIC AND NONDETERMINISTIC DECISION TREE COMPLEXITY. GLOBAL APPROACH
Fundamenta Informaticae, 1996We study the relationships between the complexity of a task description and the minimal complexity of deterministic and nondeterministic decision trees solving this task. We investigate decision trees assuming a global approach i.e. arbitrary checks from a given check system can be used for constructing decision trees.
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Approximating AC^0 by Small Height Decision Trees and a Deterministic Algorithm for #AC^0SAT [PDF]
2012 IEEE 27th Conference on Computational Complexity, 2012 We show how to approximate any function in AC^0 by decision trees of much smaller height than its number of variables. More precisely, we show that any function in n variables computable by an unbounded fan-in circuit of AND, OR, and NOT gates that has size S and depth d can be approximated by a decision tree of height n - \beta n to within error exp(-\
Srikanth Srinivasan +2 more
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2015 IEEE Symposium Series on Computational Intelligence, 2015
While collecting data from network traffic, one can create classifiers that recognize threats, anomalies, or other events. The set of labelled Net Flow records collecting traffic statistics is a very useful source of decision rules that classify the records. These rules can be created automatically using machine learning techniques.
Marcin Luckner
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While collecting data from network traffic, one can create classifiers that recognize threats, anomalies, or other events. The set of labelled Net Flow records collecting traffic statistics is a very useful source of decision rules that classify the records. These rules can be created automatically using machine learning techniques.
Marcin Luckner
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2020
In this chapter, upper bounds on the minimum complexity and algorithms for construction of deterministic decision trees for decision tables are considered. These bounds and algorithms are based on the use of so-called additive-bounded uncertainty measures for decision tables.
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In this chapter, upper bounds on the minimum complexity and algorithms for construction of deterministic decision trees for decision tables are considered. These bounds and algorithms are based on the use of so-called additive-bounded uncertainty measures for decision tables.
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A Scalable Deterministic Global Optimization Algorithm for Training Optimal Decision Tree
Advances in Neural Information Processing Systems 35, 2022Kaixun Hua, Jiayang Ren, Yankai Cao
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