Overview of use of decision tree algorithms in machine learning

Navada, Arundhati; Ansari, Aamir Nizam; Patil, Siddharth; Sonkamble, Balwant A.

doi:10.1109/icsgrc.2011.5991826

Cited by 221 publications

(97 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…They are: Classification Tree, Regression Tree, DT Forests, Classification and Regression Tree, K-Means Clustering. For example Classification Tree would find its application in probability and statistics or Regression Tree is used in calculations for real estate [18]. Some of useful machine algorithms for DTs are CHIAD, QUEST, ID3, and IDA [18,19].…”

Section: Fig 2results Of Apply Univariate Dtmentioning

confidence: 99%

Application of Decision Tree Algorithm for Data Mining in Healthcare Operations: A Case Study

SoleimanianGharehchopogh¹,

Mohammadi²,

Hakimi³

2012

IJCA

View full text Add to dashboard Cite

By means of data mining techniques, we can exploit furtive and precious information through medicine data bases. Because of huge amount of this information, study and analyses are too difficult. We want some methods to exploring through data and extract valuable information which can be used in the future similar cases. One of these cases is accouchement. The mechanism of accouchement is a natural and spontaneous process without the need to any intervention. In some conditions, maybe mother, baby or both of them are in hazard and need help and support. This help is provided by Caesarian Section which saves mother and baby. Nevertheless, we need to know when we should use surgery. This study explains utilization of medical data mining in determination of medical operation methods. We render this with accumulating 80 pregnant women information. The results show that decision tree algorithm designed for this case study generates correct prediction for more than 86.25% tests cases

show abstract

Section: Fig 2results Of Apply Univariate Dtmentioning

confidence: 99%

Application of Decision Tree Algorithm for Data Mining in Healthcare Operations: A Case Study

SoleimanianGharehchopogh¹,

Mohammadi²,

Hakimi³

2012

IJCA

View full text Add to dashboard Cite

show abstract

“…This problem has been solved successfully by IDA. It can be concluded that IDA than ID3 is more efficient and effective algorithm (Navada et al, 2011).…”

Section: Idamentioning

confidence: 94%

“…Data measurement function, it is called entropy, is used as a standard function. Entropy is determined disorder and lack of purity in a series of samples (Navada et al, 2011).…”

Section: Id3mentioning

confidence: 99%

See 1 more Smart Citation

Data mining and machine learning: an Overview of Classifiers

Haghighi¹

2015

CeN

View full text Add to dashboard Cite

At the same time of information age, digital revolution has made necessary using some of technologies to analyze most of essential information. Data mining is a technique to make sense to the available data. The aim of data mining is extracting the information from a vast volume of data and transforming them into a comprehensible form for human. For this purpose, machine learning methods are used to classify data.In this study, we discuss six popular and useful classifiers in the data mining process.

show abstract

“…DTs are exploited as a machine learning technique [11] for classifications and predictions of transmission parameters. DTs consist of a set of hierarchical rules that divide the data into groups, where a decision is made for each group.…”

Section: ) Decision-trees (Dts)mentioning

confidence: 99%