Multi-Class Classification of Agricultural Data Based on Random Forest and Feature Selection

View Sample PDF

Author(s): Lei Shi (Henan Agricultural University, China), Yaqian Qin (Zhengzhou University of Science and Technology, China), Juanjuan Zhang (Henan Agricultural University, China), Yan Wang (Zhengzhou University of Science and Technology, China), Hongbo Qiao (Henan Agricultural University, China)and Haiping Si (Henan Agricultural University, China)
Copyright: 2022
Volume: 15
Issue: 1
Pages: 17
Source title: Journal of Information Technology Research (JITR)
Editor(s)-in-Chief: Wen-Chen Hu (University of North Dakota, USA)
DOI: 10.4018/JITR.298618

Keywords: Engineering Science Reference / Information Resources Management / IT Research and Theory / Library & Information Science

Purchase

View Multi-Class Classification of Agricultural Data Based on Random Forest and Feature Selection on the publisher's website for pricing and purchasing information.

Abstract

Agricultural production and operation produce a large amount of data, which hides valuable knowledge. Data mining technology can effectively explore the connection between various factors from the massive agricultural data. Classification prediction is one of the most valuable agricultural data mining techniques. This paper presents a new algorithm consisting of machine learning algorithms, feature ranking method and instance filter, which aims to enhance the capability of the random forest algorithm and better solve the problem of agricultural multi-class classification. The performance of the new algorithm was tested by using four standard agricultural multi-class datasets, and the experimental results showed that the newly proposed method performed well on all datasets. Among them, substantial rise in classification accuracy is observed for Eucalyptus dataset. Applying random forest algorithm on Eucalyptus dataset results in classification accuracy as 53.4% and after applying the new algorithm (rough set) the classification accuracy significantly increases to 83.7%.

The IRMA Community

Research IRM

Multi-Class Classification of Agricultural Data Based on Random Forest and Feature Selection

Purchase

Abstract

Related Content

IRMA Sponsors