Deep Extreme Learning Machine: A Combined Incremental Learning Approach for Data Stream Classification

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.

HAMIDZADEH J.; MORADI M.

مرکز اطلاعات علمی 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:2022 | Volume:20 | Issue:1 Page(s): 66-72

Download Full-Text

Persian Verion

View:

629

Download:

Cites:

Information Journal Paper

Title

Deep Extreme Learning Machine: A Combined Incremental Learning Approach for Data Stream Classification

Author(s)

HAMIDZADEH J. | MORADI M. | Issue Writer Certificate

Keywords

Concept drift

data stream

extreme machine learning

incremental learning

Abstract

Streaming data refers to data that is continuously generated in the form of fast streams with high volumes. This kind of data often runs into evolving environments where a change may affect the data distribution. Because of a wide range of real-world applications of data streams, performance improvement of streaming analytics has become a hot topic for researchers. The proposed method integrates online ensemble learning into extreme machine learning to improve the data stream classification performance. The proposed incremental method does not need to access the samples of previous blocks. Also, regarding the AdaBoost approach, it can react to Concept drift by the component weighting mechanism and component update mechanism. The proposed method can adapt to the changes, and its performance is leveraged to retain high-accurate classifiers. The experiments have been done on benchmark datasets. The proposed method can achieve 0. 90% average specificity, 0. 69% average sensitivity, and 0. 87% average accuracy, indicating its superiority compared to two competing methods.

Cites

No record.

References

No record.

Cite

APA: Copy

HAMIDZADEH, J., & MORADI, M.. (2022). Deep Extreme Learning Machine: A Combined Incremental Learning Approach for Data Stream Classification. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR, 20(1 ), 66-72. SID. https://sid.ir/paper/960124/en

Vancouver: Copy

HAMIDZADEH J., MORADI M.. Deep Extreme Learning Machine: A Combined Incremental Learning Approach for Data Stream Classification. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR[Internet]. 2022;20(1 ):66-72. Available from: https://sid.ir/paper/960124/en

IEEE: Copy

J. HAMIDZADEH, and M. MORADI, “Deep Extreme Learning Machine: A Combined Incremental Learning Approach for Data Stream Classification,” NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR, vol. 20, no. 1 , pp. 66–72, 2022, [Online]. Available: https://sid.ir/paper/960124/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops