MULTI-GROUP CLASSIFICATION USING INTERVAL LINEAR PROGRAMMING

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.

IZADI B.; RANJBARIAN B.; KETABI S.; NASSIRI MOFAKHAM F.

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

Journal Paper

Paper Information

Journal: IRANIAN JOURNAL OF OPERATIONS RESEARCH Year:2013 | Volume:4 | Issue:1 Page(s): 55-74

Download Full-Text

View:

361

Download:

165

Cites:

Information Journal Paper

Title

MULTI-GROUP CLASSIFICATION USING INTERVAL LINEAR PROGRAMMING

Author(s)

IZADI B. | RANJBARIAN B. | KETABI S. | NASSIRI MOFAKHAM F. | Issue Writer Certificate

Keywords

MULTI-GROUP INTERVAL LINEAR PROGRAMMING

CLASSIFICATION PROBLEM

FUZZY DELPHI FEATURE SELECTION

Abstract

Among various statistical and data mining discriminant analysis proposed so far for group classification, linear programming discriminant analysis has recently attracted the researchers’ interest. This study evaluates multi-group discriminant linear programming (MDLP) for CLASSIFICATION PROBLEMs against well-known methods such as neural networks and support vector machine. MDLP is less complicated as compared to other methods and does not suffer from having local optima. This study also proposes a fuzzy Delphi method to select and gather the required data, when databases suffer from deficient data. In addition, to absorb the uncertainty infused to collecting data, interval MDLP (IMDLP) is developed. The results show that the performance of MDLP and specially IMDLP is better than conventional classification methods with respect to correct classification, at least for small and medium-size datasets.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

APA: Copy

IZADI, B., RANJBARIAN, B., KETABI, S., & NASSIRI MOFAKHAM, F.. (2013). MULTI-GROUP CLASSIFICATION USING INTERVAL LINEAR PROGRAMMING. IRANIAN JOURNAL OF OPERATIONS RESEARCH, 4(1), 55-74. SID. https://sid.ir/paper/308456/en

Vancouver: Copy

IZADI B., RANJBARIAN B., KETABI S., NASSIRI MOFAKHAM F.. MULTI-GROUP CLASSIFICATION USING INTERVAL LINEAR PROGRAMMING. IRANIAN JOURNAL OF OPERATIONS RESEARCH[Internet]. 2013;4(1):55-74. Available from: https://sid.ir/paper/308456/en

IEEE: Copy

B. IZADI, B. RANJBARIAN, S. KETABI, and F. NASSIRI MOFAKHAM, “MULTI-GROUP CLASSIFICATION USING INTERVAL LINEAR PROGRAMMING,” IRANIAN JOURNAL OF OPERATIONS RESEARCH, vol. 4, no. 1, pp. 55–74, 2013, [Online]. Available: https://sid.ir/paper/308456/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops