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IEEE Transactions on Knowledge and Data Engineering, 2021
Current decision trees such as C4.5 and CART are widely used in different fields due to their simplicity, accuracy and intuitive interpretation. Similar to other popular classifiers, these tree-based classification algorithms are developed for fixed-length vector data and suffer from intrinsic limitations in handling complex data such as sequences.
Zengyou He +4 more
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Current decision trees such as C4.5 and CART are widely used in different fields due to their simplicity, accuracy and intuitive interpretation. Similar to other popular classifiers, these tree-based classification algorithms are developed for fixed-length vector data and suffer from intrinsic limitations in handling complex data such as sequences.
Zengyou He +4 more
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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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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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Knowledge-Based Systems, 2002
In this paper, a hybrid learning approach named hybrid decision tree (HDT) is proposed. HDT simulates human reasoning by using symbolic learning to do qualitative analysis and using neural learning to do subsequent quantitative analysis. It generates the trunk of a binary HDT according to the binary information gain ratio criterion in an instance space
Zhi-Hua Zhou, Zhaoqian Chen
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In this paper, a hybrid learning approach named hybrid decision tree (HDT) is proposed. HDT simulates human reasoning by using symbolic learning to do qualitative analysis and using neural learning to do subsequent quantitative analysis. It generates the trunk of a binary HDT according to the binary information gain ratio criterion in an instance space
Zhi-Hua Zhou, Zhaoqian Chen
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Optimization of the decision tree
[Proceedings] Third International Conference on Tools for Artificial Intelligence - TAI 91, 2002An 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 +2 more
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Decision Trees and Applications
2020In 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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2011 International Conference on Computer Vision, 2011
This paper introduces a new formulation for discrete image labeling tasks, the Decision Tree Field (DTF), that combines and generalizes random forests and conditional random fields (CRF) which have been widely used in computer vision. In a typical CRF model the unary potentials are derived from sophisticated random forest or boosting based classifiers,
Sebastian Nowozin +5 more
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This paper introduces a new formulation for discrete image labeling tasks, the Decision Tree Field (DTF), that combines and generalizes random forests and conditional random fields (CRF) which have been widely used in computer vision. In a typical CRF model the unary potentials are derived from sophisticated random forest or boosting based classifiers,
Sebastian Nowozin +5 more
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Journal of the ACM, 1990
The reduction algorithm is a technique for improving a decision tree in the abseence of aproecise cost criterion. The result of applying the algorithm is an irreducible tree that is no less efficient than the original, and may be more efficient. Irreducible trees arise in discrete decision theory as an algebraic form for decision trees.
J. Robin B. Cockett, J. A. Hierrera
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The reduction algorithm is a technique for improving a decision tree in the abseence of aproecise cost criterion. The result of applying the algorithm is an irreducible tree that is no less efficient than the original, and may be more efficient. Irreducible trees arise in discrete decision theory as an algebraic form for decision trees.
J. Robin B. Cockett, J. A. Hierrera
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Decision Trees for Decision Tables
1994We 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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Intelligent Data Analysis, 2000
The article presents the extension of a common decision tree concept to a multidimensional -- vector -- decision tree constructed with the help of evolutionary techniques. In contrary to the common decision tree the vector decision tree can make more than just one suggestion per input sample.
Matej Sprogar +4 more
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The article presents the extension of a common decision tree concept to a multidimensional -- vector -- decision tree constructed with the help of evolutionary techniques. In contrary to the common decision tree the vector decision tree can make more than just one suggestion per input sample.
Matej Sprogar +4 more
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Knowledge and Information Systems, 2008
Ranking problems have recently become an important research topic in the joint field of machine learning and information retrieval. This paper presented a new splitting rule that introduces a metric, i.e., an impurity measure, to construct decision trees for ranking tasks.
Fen Xia +3 more
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Ranking problems have recently become an important research topic in the joint field of machine learning and information retrieval. This paper presented a new splitting rule that introduces a metric, i.e., an impurity measure, to construct decision trees for ranking tasks.
Fen Xia +3 more
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