Decision Tree Modeling

Brown, Steven D.; Myles, Anthony J.

doi:10.1016/b978-0-12-409547-2.00653-3

Cited by 5 publications

(3 citation statements)

References 113 publications

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“…Here “|Dj|/|D| ” j. the weight of the section, the probability that a random variable in the data set “ Pi ” belongs to a class, “ Info (Dj) ” j. The amount of information required to define the class label of D, “ v ” refers to the number of discrete values in the A attribute 14‐16

Gain Ratio false(normalA false) = \frac{\sum_{i = 1}^{m} Pix \log_{2} Pi - \sum_{j = 1}^{v} \frac{(||, D_{j})}{| D |} xInfo (D_{j})}{- \sum_{j = 1}^{v} \frac{(||, D_{j})}{| D |} x \log_{2} (\frac{|D_{j}|}{| D |})} .

…”

Section: Methodsmentioning

confidence: 99%

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Application of deep learning and classical machine learning methods in the diagnosis of attention deficit hyperactivity disorder according to temperament features

Altun

Alkan

Altun

2022

Concurrency and Computation

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Attention deficit hyperactivity disorder (ADHD) is a common childhood neurodevelopmental disorder with symptoms of attention deficit, hyperactivity and impulsivity, with a prevalence of 8%-12% worldwide. Various studies have revealed that there may be a relationship between ADHD and temperament traits in the etiology of ADHD, which has a multifactorial etiology. According to our knowledge, there is no study in the literature that determines the use of machine learning methods in diagnosing ADHD uses a data set created with temperament characteristics. Different methods were used in this first study. The study included 60 ADHD patients and 60 control group children. The test scores of these children were collected from the Department of Child and Adolescent Psychiatry, Kahramanmaraş Sütçü İmam University Medical Faculty, after obtaining the necessary ethics committee permission. ADHD diagnosis was made according to DSM-5 classification. According to temperament characteristics, the highest classification success in ADHD diagnosis was calculated as 92.5% in decision tree method. Long short term memory (LSTM), one of the deep learning methods, achieved 88% classification success. The success of both methods is quite high and they have been compared with some ADHD classification studies in the literature.

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Gain Ratio false(normalA false) = \frac{\sum_{i = 1}^{m} Pix \log_{2} Pi - \sum_{j = 1}^{v} \frac{(||, D_{j})}{| D |} xInfo (D_{j})}{- \sum_{j = 1}^{v} \frac{(||, D_{j})}{| D |} x \log_{2} (\frac{|D_{j}|}{| D |})} .

…”

Section: Methodsmentioning

confidence: 99%

“…The amount of information required to define the class label of D, "v" refers to the number of discrete values in the A attribute. [14][15][16] Gain Ratio…”

Section: • No Attributes Leftmentioning

confidence: 99%

Application of deep learning and classical machine learning methods in the diagnosis of attention deficit hyperactivity disorder according to temperament features

Altun

Alkan

Altun

2022

Concurrency and Computation

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“…On the basis of Apriori algorithm, the improvement degree parameter of rules is used as the measurement standard of importance [5]. Once the frequent itemsets are found by the transactions in database d, it is straightforward to generate strong association rules from them.…”

Section: New Attributes Generation Based On Association Rulesmentioning

confidence: 99%

A Model of Talent Management in Universities Based on Decision Tree and Correlation Analysis

Wu,

Liu

2024

Advances in Transdisciplinary Engineering

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Traditional human resource evaluation methods rely too much on people’s subjective judgment and experience. In order to overcome this defect, a talent management model based on decision tree and correlation analysis is proposed. The scheme uses the concept of decision tree, classifier and correlation analysis to preprocess the primary data. Then Apriori association rules and C4.5 algorithm are adopted to complete the combination prediction of the two, which reduces the efficiency of the algorithm to a certain extent when searching for approximate high reliability rules, and makes the improved decision tree algorithm have good scalability. Finally, the model is tested and analyzed through the actual data in human resource management in universities. The results show that the scheme can improve the prediction accuracy of the algorithm and provide a theoretical basis for scientific decision-making of leaders and functional departments.

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Feature Selection Using Extra Trees Classifier for Research Productivity Framework in Indonesia

Sanmorino

Marnisah

Sunardi

2023

Lecture Notes in Electrical Engineering

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Decision Tree Modeling

Cited by 5 publications

References 113 publications

Application of deep learning and classical machine learning methods in the diagnosis of attention deficit hyperactivity disorder according to temperament features

Application of deep learning and classical machine learning methods in the diagnosis of attention deficit hyperactivity disorder according to temperament features

A Model of Talent Management in Universities Based on Decision Tree and Correlation Analysis

Feature Selection Using Extra Trees Classifier for Research Productivity Framework in Indonesia

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