Construction of Scalable Decision Tree Based on Fast Data Partitioning and Pre-Pruning

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

LOTFI S.; GHASEMZADEH M.; MOHSENZADEH M.; Mirzarezaee M.

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR Year:2021 | Volume:19 | Issue:1 Page(s): 52-58

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

263

Download:

Cites:

Information Journal Paper

Title

Construction of Scalable Decision Tree Based on Fast Data Partitioning and Pre-Pruning

Author(s)

LOTFI S. | GHASEMZADEH M. | MOHSENZADEH M. | Mirzarezaee M. | Issue Writer Certificate

Keywords

Pre-pruningQ1

data miningQ1

decision treeQ1

scalableQ1

Abstract

Classification is one of the most important tasks in data mining and machine learning; and the decision tree, as one of the most widely used classification algorithms, has the advantage of simplicity and the ability to interpret results more easily. But when dealing with huge amounts of data, the obtained decision tree would grow in size and complexity, and therefore require excessive running time. Almost all of the tree-construction algorithms need to store all or part of the training data set; but those algorithms which do not face memory shortages because of selecting a subset of data, can save the extra time for data selection. In order to select the best feature to create a branch in the tree, a lot of calculations are required. In this paper we presents an incremental scalable approach based on fast partitioning and pruning; The proposed algorithm builds the decision tree via using the entire training data set but it doesn't require to store the whole data in the main memory. The Pre-pruning method has also been used to reduce the complexity of the tree. The experimental results on the UCI data set show that the proposed algorithm, in addition to preserving the competitive accuracy and construction time, could conquer the mentioned disadvantages of former methods.

Cites

No record.

References

No record.

Cite

APA: Copy

LOTFI, S., GHASEMZADEH, M., MOHSENZADEH, M., & Mirzarezaee, M.. (2021). Construction of Scalable Decision Tree Based on Fast Data Partitioning and Pre-Pruning. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR, 19(1 ), 52-58. SID. https://sid.ir/paper/390645/en

Vancouver: Copy

LOTFI S., GHASEMZADEH M., MOHSENZADEH M., Mirzarezaee M.. Construction of Scalable Decision Tree Based on Fast Data Partitioning and Pre-Pruning. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR[Internet]. 2021;19(1 ):52-58. Available from: https://sid.ir/paper/390645/en

IEEE: Copy

S. LOTFI, M. GHASEMZADEH, M. MOHSENZADEH, and M. Mirzarezaee, “Construction of Scalable Decision Tree Based on Fast Data Partitioning and Pre-Pruning,” NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR, vol. 19, no. 1 , pp. 52–58, 2021, [Online]. Available: https://sid.ir/paper/390645/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops