Results 231 to 240 of about 240,542 (264)

Decision Trees for Decision Tables

1994
We investigate decision trees for decision tables. We present some upper and lower bounds on the minimal decision tree depth. These bounds are expressed by some parameters of decision rule systems constructed for decision tables.
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Decision trees with AND, OR queries

Proceedings of Structure in Complexity Theory. Tenth Annual IEEE Conference, 2002
We investigate decision trees in which one is allowed to query threshold functions of subsets of variables. We are mainly interested in the case where only queries of AND and OR are allowed. This model is a generalization of the classical decision tree model.
Yosi Ben-Asher, Ilan Newman
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Optimization of the decision tree

[Proceedings] Third International Conference on Tools for Artificial Intelligence - TAI 91, 2002
An approach is presented to the optimization of decision trees. A decision tree is considered optimal if it correctly classifies the known data set and has the minimal number of nodes. It is shown that it is important to decide the right order of attributes to test, for this can reduce the number of checking nodes in a decision tree. >
Won Chan Jung, J. Bush Jones, Jianhua Chen 0003   +2 more
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Fisher’s decision tree

Expert Systems with Applications, 2013
Univariate decision trees are classifiers currently used in many data mining applications. This classifier discovers partitions in the input space via hyperplanes that are orthogonal to the axes of attributes, producing a model that can be understood by human experts.
Asdrúbal López-Chau, Jair Cervantes, Lourdes López-García, Farid García-Lamont   +3 more
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On the Decision Trees with Symmetries

2018
We introduce a propositional proof system based on decision trees utilizing symmetries of formulas. We refer to this proof system as decision trees with symmetries (\(\mathrm {SDT}\)). \(\mathrm {SDT}\) can be polynomially simulated by the proof system \(\mathrm {SR}\text {-}\mathrm {I}\) introduced by Krishnamurthy [7]; \(\mathrm {SR}\text {-}\mathrm {
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Decision Trees and Applications

2020
In many cases, the meaning of information is wrongly related to either the sense of data or the notion of knowledge. There is a crucial sequence of steps before information becomes knowledge and the value of data depends in the existence of information so as to produce knowledge.
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On decision trees for orthants

Information Processing Letters, 1997
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Distributed Decision Tree

Proceedings of the 9th Annual ACM India Conference, 2016
Decision Tree is a tree-structured plan of a set of attributes to test in order to predict the output. MapReduce and Spark is a programming model used for processing data on a distributed file system. In this paper, MapReduce and Spark implementation of Decision Tree is named as Distributed Decision Tree (DDT) and Spark Tree (ST) respectively. Decision
Ankit Desai, Sanjay Chaudhary
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Oncology Decision Tree

Collegian, 2000
Financial cutbacks and budgetary constraints continue to be a part of government policy, and impact deeply on many aspects of daily life in Australia in the late 90s. Health care is not immune from these measures, and nurses and other allied health professionals are having to manage increasingly complex patient care with less funding and resources ...
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A Soft Decision Tree

2002
Searching for binary partition of attribute domains is an important task in Data Mining, particularly in decision tree methods. The most important advantage of decision tree methods are based on compactness and clearness of presented knowledge and high accuracy of classification.
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