Classifying Continuous Data Set by ID3 Algorithm

Jearanaitanakij, Kietikul

doi:10.1109/icics.2005.1689212

Cited by 6 publications

(2 citation statements)

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“…Up to now, ID3 algorithm has been widely used in real world applications [9], [10], [11]. It has played an important role to enhance the performance of induction algorithm.…”

Section: Introductionmentioning

confidence: 99%

General criteria on building decision trees for data classification

Huang

Hoa

2009

Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human

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Decision trees have been widely and successfully applied to data mining and machine learning for data classification. One of the critical components in a decision tree algorithm is the criterion used to select which attribute will become a test attribute in a given branch of the tree. Several algorithms, such as ID3, C4.5, and CART, are investigated and compared for determining attributes as the root and the branches in decision trees. This paper presents the issues of traditional ID3 algorithm that directly affect building decision trees. The goal is to select critical factors in ID3 algorithm that result in an efficient decision tree for classification applications. Examples are given to illustrate what factors may affect the construction of decision trees.

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“…Up to now, ID3 algorithm has been widely used in real world applications [9], [10], [11]. It has played an important role to enhance the performance of induction algorithm.…”

Section: Introductionmentioning

confidence: 99%

General criteria on building decision trees for data classification

Huang

Hoa

2009

Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human

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“…ID3 and C4.5 are very similar methods, but have a few differences. For example, the C4.5 algorithm allows the usage of both continuous and discrete attributes, whereas the ID3 algorithm has difficulty dealing with continuous data since it is more intensive to find a proper split on this kind of attribute [16]. Tree classifiers use supervised learning methods to organize data results into a hierarchical tree, with each node correlating to a different attribute.…”

Section: B Data Analysismentioning

confidence: 99%

Knowledge Extraction from Metacognitive Reading Strategies Data Using Induction Trees

Taylor¹,

Kulkarni²,

Mokhtari³

2016

ijacsa

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Abstract-The assessment of students' metacognitive knowledge and skills about reading is critical in determining their ability to read academic texts and do so with comprehension. In this paper, we used induction trees to extract metacognitive knowledge about reading from a reading strategies dataset obtained from a group of 1636 undergraduate college students. Using a C4.5 algorithm, we constructed decision trees, which helped us classify participants into three groups based on their metacognitive strategy awareness levels consisting of global, problem-solving and support reading strategies. We extracted rules from these decision trees, and in order to evaluate accuracy of the extracted rules, we built a fuzzy inference system (FIS) with the extracted rules as a rule base and classified the test dataset with the FIS. The extracted rules are evaluated using measures such as the overall efficiency and Kappa coefficient.

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