IMPROVING CLASSIFICATION PERFORMANCE USING COMBINATION FEATURES OF DIFFERENT NEURAL NETWORK ENSEMBLE METHODS

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.

MOHAMMADZADEH JAVAD; MASOUDNIA SAEED; ARAANY ALI

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

Journal Paper

Paper Information

Journal: SIGNAL AND DATA PROCESSING Year:2011 | Volume:- | Issue:2 (SERIAL 16) Page(s): 101-114

Download Full-Text

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

Persian Verion

View:

686

Download:

Cites:

Information Journal Paper

Title

IMPROVING CLASSIFICATION PERFORMANCE USING COMBINATION FEATURES OF DIFFERENT NEURAL NETWORK ENSEMBLE METHODS

Author(s)

MOHAMMADZADEH JAVAD | MASOUDNIA SAEED | ARAANY ALI | Issue Writer Certificate

Keywords

CLASSIFICATIONQ2

NEURAL NETWORKS ENSEMBLEQ3

NEGATIVE CORRELATION LEARNINGQ3

MIXTURE OF EXPERTSQ3

Abstract

Negative Correlation Learning (NCL) and MIXTURE OF EXPERTS (ME), two popular combining methods, each employ different special error functions for the simultaneous training of NN experts to produce negatively correlated NN experts. In this paper, we review the properties of the NCL and ME methods, discussing their advantages and disadvantages. Characterization of both methods showed that they have different but complementary features, so if a hybrid system can be designed to include features of both NCL and ME, it may be better than each of its basis approaches. In this study, an approach is proposed to combine the features of both methods, i.e., Mixture of Negatively Correlated Experts (MNCE). In this approach, the capability of a control parameter for NCL is incorporated in the error function of ME, which enables the training algorithm of ME to establish better balance in bias-variance-covariance trade-offs. The proposed hybrid ensemble methods, MNCE, are compared with their constituent methods, ME and NCL, in solving several benchmark problems. The experimental results show that our proposed method preserve the advantages and alleviate the disadvantages of their basis approaches, offering significantly improved performance over the original methods.

Cites

No record.

References

No record.

Cite

APA: Copy

MOHAMMADZADEH, JAVAD, MASOUDNIA, SAEED, & ARAANY, ALI. (2011). IMPROVING CLASSIFICATION PERFORMANCE USING COMBINATION FEATURES OF DIFFERENT NEURAL NETWORK ENSEMBLE METHODS. SIGNAL AND DATA PROCESSING, -(2 (SERIAL 16)), 101-114. SID. https://sid.ir/paper/160687/en

Vancouver: Copy

MOHAMMADZADEH JAVAD, MASOUDNIA SAEED, ARAANY ALI. IMPROVING CLASSIFICATION PERFORMANCE USING COMBINATION FEATURES OF DIFFERENT NEURAL NETWORK ENSEMBLE METHODS. SIGNAL AND DATA PROCESSING[Internet]. 2011;-(2 (SERIAL 16)):101-114. Available from: https://sid.ir/paper/160687/en

IEEE: Copy

JAVAD MOHAMMADZADEH, SAEED MASOUDNIA, and ALI ARAANY, “IMPROVING CLASSIFICATION PERFORMANCE USING COMBINATION FEATURES OF DIFFERENT NEURAL NETWORK ENSEMBLE METHODS,” SIGNAL AND DATA PROCESSING, vol. -, no. 2 (SERIAL 16), pp. 101–114, 2011, [Online]. Available: https://sid.ir/paper/160687/en

Related Journal Papers