Decision Tree Algorithms C4.5 and C5.0 in Data Mining: A Review

Arif, Muhammad

doi:10.14257/ijdta.2018.11.1.01

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…below shows the accuracy of the C5 and C4.5 comparisons. Display speed and memory [5]. Comparison of C 5 and C4.5[5] C5.0's algorithm is faster contrasted to C4.5 in computing.…”

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confidence: 99%

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Analyzing the Comparison of C4.5, CART and C5.0 Algorithms on Heart Disease Dataset using Decision Tree Method

Myint¹,

Tin

2021

Proceedings of the 2nd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2020, 27-28 Feb

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Data acquisition methods can be expected for patients suffering from heart disease. The resolution of this learning was to compare a similar data mining algorithm to the calculation of heart diseaseThis research paper proposed the traditional decision tree procedure and weighted decision tree procedure. Traditional decision tree process consists of C4.5, C5.0, CART processes. The weighted decision process is established suitable weights of training cases based on naïve Bayesian theorem before trying to construct a decision tree model. The main objectives of this research paper are (1) to know the operation of C4.5 process, Cart and C5.0 process, (2) to learn how to analysis the traditional decision tree and weighted decision tree algorithms are compared results from both training and testing dataset for heart disease.

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“…below shows the accuracy of the C5 and C4.5 comparisons. Display speed and memory [5]. Comparison of C 5 and C4.5[5] C5.0's algorithm is faster contrasted to C4.5 in computing.…”

mentioning

confidence: 99%

“…Display speed and memory [5]. Comparison of C 5 and C4.5[5] C5.0's algorithm is faster contrasted to C4.5 in computing. Decision Tree evaluation is a diagram that is not exact, Displayed in leaves and seconds.…”

mentioning

confidence: 99%

Analyzing the Comparison of C4.5, CART and C5.0 Algorithms on Heart Disease Dataset using Decision Tree Method

Myint¹,

Tin

2021

Proceedings of the 2nd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2020, 27-28 Feb

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Automatic Eligibility of Sellers in an Online Marketplace: A Case Study of Amazon Algorithm

2022

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Purchase processes on Amazon Marketplace begin at the Buy Box, which represents the buy click process through which numerous sellers compete. This study aimed to estimate empirically the relevant seller characteristics that Amazon could consider featuring in the Buy Box. To that end, 22 product categories from Italy’s Amazon web page were studied over a ten-month period, and the sellers were analyzed through their products featured in the Buy Box. Two different experiments were proposed and the results were analyzed using four classification algorithms (a neural network, random forest, support vector machine, and C5.0 decision trees) and a rule-based classification. The first experiment aimed to characterize sellers unspecifically by predicting their change at the Buy Box. The second one aimed to predict which seller would be featured in it. Both experiments revealed that the customer experience and the dynamics of the sellers’ prices were important features of the Buy Box. Additionally, we proposed a set of default features that Amazon could consider when no information about sellers was available. We also proposed the possible existence of a relationship or composition among important features that could be used for sellers to be featured in the Buy Box.

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Decision Tree Algorithms C4.5 and C5.0 in Data Mining: A Review

Abstract: Abstract

Cited by 2 publications

References 12 publications

Analyzing the Comparison of C4.5, CART and C5.0 Algorithms on Heart Disease Dataset using Decision Tree Method

Analyzing the Comparison of C4.5, CART and C5.0 Algorithms on Heart Disease Dataset using Decision Tree Method

Automatic Eligibility of Sellers in an Online Marketplace: A Case Study of Amazon Algorithm

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