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Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/10459
Title: | A Hybrid Ensemble for Classification in Multiclass Datasets : An Application to Oilseed Disease Dataset |
Other Titles: | Not Available |
Authors: | Archana Chaudhary Savita Kolhe Raj Kamal |
ICAR Data Use Licennce: | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf |
Author's Affiliated institute: | ICAR-Indian Institute of Soybean Research, Indore |
Published/ Complete Date: | 2016-04-09 |
Project Code: | Not Available |
Keywords: | Machine learning, Multiclass classification, Hybrid ensemble, Oilseed disease |
Publisher: | Elsevier Pub |
Citation: | Not Available |
Series/Report no.: | Not Available; |
Abstract/Description: | The paper presents a new hybrid ensemble approach consisting of a combination of machine learning algorithms, a feature ranking method and a supervised instance filter. Its aim is to improve the performance results of machine learning algorithms for multiclass classification problems. The performance of new hybrid ensemble approach is tested for its effectiveness over four standard agriculture multiclass datasets. It performs better on all these datasets. It is applied on multiclass oilseed disease dataset. It is observed that ensemble-Vote performs better than Logistic Regression and Naïve Bayes algorithms. The performance results of hybrid ensemble are compared with ensemble-Vote. The performance results prove that the new hybrid ensemble approach outperforms ensemble-Vote with improved oilseed disease classification accuracy up to 94.73%. |
Description: | Not Available |
ISSN: | Not Available |
Type(s) of content: | Research Paper |
Sponsors: | Not Available |
Language: | English |
Name of Journal: | Computers and Electronics in Agriculture |
NAAS Rating: | 9.86 |
Volume No.: | 124 |
Page Number: | 65-72 |
Name of the Division/Regional Station: | Not Available |
Source, DOI or any other URL: | Not Available |
URI: | http://krishi.icar.gov.in/jspui/handle/123456789/10459 |
Appears in Collections: | CS-DSBR-Publication |
Files in This Item:
File | Description | Size | Format | |
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COMPAG.pdf | 1.4 MB | Adobe PDF | View/Open |
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