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Topic

Decision tree model

About: Decision tree model is a(n) research topic. Over the lifetime, 2256 publication(s) have been published within this topic receiving 38142 citation(s).
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Journal ArticleDOI
TL;DR: Several properties of the graph-theoretic complexity are proved which show, for example, that complexity is independent of physical size and complexity depends only on the decision structure of a program.
Abstract: This paper describes a graph-theoretic complexity measure and illustrates how it can be used to manage and control program complexity. The paper first explains how the graph-theory concepts apply and gives an intuitive explanation of the graph concepts in programming terms. The control graphs of several actual Fortran programs are then presented to illustrate the correlation between intuitive complexity and the graph-theoretic complexity. Several properties of the graph-theoretic complexity are then proved which show, for example, that complexity is independent of physical size (adding or subtracting functional statements leaves complexity unchanged) and complexity depends only on the decision structure of a program.

4,749 citations


Journal ArticleDOI
16 Nov 2002
TL;DR: This work presents a new method for proving strong lower bounds in communication complexity based on the notion of the conditional information complexity of a function, and shows that it also admits a direct sum theorem.
Abstract: We present a new method for proving strong lower bounds in communication complexity. This method is based on the notion of the conditional information complexity of a function which is the minimum amount of information about the inputs that has to be revealed by a communication protocol for the function. While conditional information complexity is a lower bound on the communication complexity, we show that it also admits a direct sum theorem. Direct sum decomposition reduces our task to that of proving (conditional) information complexity lower bounds for simple problems (such as the AND of two bits). For the latter, we develop novel techniques based on Hellinger distance and its generalizations.

711 citations


Journal ArticleDOI
TL;DR: Several complexity measures for Boolean functions are discussed: certificate complexity, sensitivity, block sensitivity, and the degree of a representing or approximating polynomial, and how they give bounds for the decision tree complexity of Boolean functions on deterministic, randomized, and quantum computers.
Abstract: We discuss several complexity measures for Boolean functions: certificate complexity, sensitivity, block sensitivity, and the degree of a representing or approximating polynomial. We survey the relations and biggest gaps known between these measures, and show how they give bounds for the decision tree complexity of Boolean functions on deterministic, randomized, and quantum computers.

704 citations


Journal ArticleDOI
01 Jul 1987-Nature
TL;DR: Information-based complexity seeks to develop general results about the intrinsic difficulty of solving problems where available information is partial or approximate and to apply these results to specific problems.
Abstract: Information-based complexity seeks to develop general results about the intrinsic difficulty of solving problems where available information is partial or approximate and to apply these results to specific problems. This allows one to determine what is meant by an optimal algorithm in many practical situations, and offers a variety of interesting and sometimes surprising theoretical results.

615 citations


Journal ArticleDOI
TL;DR: This paper compares five methods for pruning decision trees, developed from sets of examples, and shows that three methods—critical value, error complexity and reduced error—perform well, while the other two may cause problems.
Abstract: This paper compares five methods for pruning decision trees, developed from sets of examples. When used with uncertain rather than deterministic data, decision-tree induction involves three main stages—creating a complete tree able to classify all the training examples, pruning this tree to give statistical reliability, and processing the pruned tree to improve understandability. This paper concerns the second stage—pruning. It presents empirical comparisons of the five methods across several domains. The results show that three methods—critical value, error complexity and reduced error—perform well, while the other two may cause problems. They also show that there is no significant interaction between the creation and pruning methods.

607 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20222
2021101
2020163
2019158
2018121
201790