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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
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Journal Article
TL;DR: Decision tree inductive learning and data mining is an important method and C4.5 algorithm is applied to the results of classification in information retrieval, search results to achieve a hierarchical classification.
Abstract: Decision tree inductive learning and data mining is an important method.In this paper,c4.5 algorithm,decision tree construction and pruning were introduced.Then C4.5 algorithm is applied to the results of classification in information retrieval,search results to achieve a hierarchical classification.

1 citations

Proceedings ArticleDOI
01 Nov 2006
TL;DR: The model for random BFs is shown to have strong descriptive power for the benchmark data and is promoted as a method of predicting, for a given BF, circuit complexity measures such as the area of a VLSI implementation.
Abstract: It has been shown that when Binary Decision Diagrams (BDDs) are formed from uniformly distributed random Boolean Functions (BFs), the average number of nodes in the BDDs is in a simple relation to the number of variables and terms in the BFs. In the present work, the node counts for BBDs formed from ISCAS benchmark circuits are examined and compared to the results for random BFs. The model for random BFs is shown to have strong descriptive power for the benchmark data. Therefore, the model is promoted as a method of predicting, for a given BF, circuit complexity measures such as the area of a VLSI implementation..

1 citations

01 Jan 2008
TL;DR: A new model of representing semantics called the concept relational tree model is proposed, which adapts the architecture of expression tree in providing a hierarchical organization to semantics and enables a more flexible way of controlling the structure of semantics.
Abstract: Summary In this paper, a new model of representing semantics called the concept relational tree model is proposed. The model adapts the architecture of expression tree in providing a hierarchical organization to semantics. It is motivated by an approach known as the discourse structure tree that organizes semantics based on preposition and relies on rhetorical relations to define the connection between the prepositions. Comparatively, the concept relational tree uses concept and relation as its organizational unit instead. This allows the semantics of text to be defined at a finer level, which consequently enables a more flexible way of controlling the structure of semantics.

1 citations

Proceedings Article
Y. W. Teh1
01 Jan 2004
TL;DR: The study concludes that both attributes and tuples, are important factors to be considered to improve the response time of a query.
Abstract: With the availability of very large data storage today, redundant data structures are no longer a big issue. However, an intelligent way of managing materialised projection and selection views that can lead to fast access of data is the central issue dealt with in this paper. A set of implementation steps for the data warehouse administrators or decision makers to improve the response time of queries is also defined. The study concludes that both attributes and tuples, are important factors to be considered to improve the response time of a query. The adoption of data mining techniques in the physical design of data warehouses has been shown to be useful in practice.

1 citations

Patent
30 May 2019
TL;DR: In this article, a method of and a system for training and using a Machine Learning Algorithm (MLA), the MLA using a decision tree model having decision tree is described.
Abstract: There is disclosed a method of and a system for training and using a Machine Learning Algorithm (MLA), the MLA using a decision tree model having a decision tree. During training a training object being associated with a categorical feature and is processed at a node of the decision tree. The method comprises calculating a numeric representation of the categorical feature and the value of the splits for the node “in-line” with generating a given iteration of the decision tree.

1 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202310
202224
2021101
2020163
2019158
2018121