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Semantic Scholar Open Access 2024 40 sitasi

An Ensemble Method for Data Classification Using State-of-the-art Methodologies

Sujata Ray Debasmita Pradhan N. Ray
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Abstrak

Over the past few decades, classification has consistently posed a significant computational challenge. This study presents an innovative ensemble classification model designed for data classification, drawing inspiration from Radial Basis Function, Extreme Learning Machine, Functional Linked Artificial Neural Network, and Artificial Neural Network. The study involved experimenting with various combinations of ensemble methods to construct an ensemble classifier. Remarkably, implementing the ensemble model using Radial Basis Function Network (RBFN), Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and Functional Linked Artificial Neural Network (FLANN) yielded superior results when tested on benchmark datasets. The accuracy range of the ensemble method varies from 80% to 98% which is a good performance considering the diverge data sets used.

Penulis (3)

S

Sujata Ray

D

Debasmita Pradhan

N

N. Ray

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APA MLA BibTeX
Ray, S., Pradhan, D., Ray, N. (2024). An Ensemble Method for Data Classification Using State-of-the-art Methodologies. https://doi.org/10.1109/ESIC60604.2024.10481631

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Informasi Jurnal
Tahun Terbit
2024
Bahasa
en
Total Sitasi
40×
Sumber Database
Semantic Scholar
DOI
10.1109/ESIC60604.2024.10481631
Akses
Open Access ✓