A Prediction Model Based on Relevance Vector Machine and Granularity Analysis
- Authors
- Cho, Young Im
- Issue Date
- 25-Sep-2016
- Publisher
- KOREAN INST INTELLIGENT SYSTEMS
- Keywords
- Quotient space theory; Granular computing; RVM; Grey model
- Citation
- INTERNATIONAL JOURNAL OF FUZZY LOGIC AND INTELLIGENT SYSTEMS, v.16, no.3, pp.157 - 162
- Journal Title
- INTERNATIONAL JOURNAL OF FUZZY LOGIC AND INTELLIGENT SYSTEMS
- Volume
- 16
- Number
- 3
- Start Page
- 157
- End Page
- 162
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/7877
- DOI
- 10.5391/IJFIS.2016.16.3.157
- ISSN
- 1598-2645
- Abstract
- In this paper, a yield prediction model based on relevance vector machine (RVM) and a granular computing model (quotient space theory) is presented. With a granular computing model, massive and complex meteorological data can be analyzed at different layers of different grain sizes, and new meteorological feature data sets can be formed in this way. In order to forecast the crop yield, a grey model is introduced to label the training sample data sets, which also can be used for computing the tendency yield. An RVM algorithm is introduced as the classification model for meteorological data mining. Experiments on data sets from the real world using this model show an advantage in terms of yield prediction compared with other models.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - IT융합대학 > 컴퓨터공학과 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/7877)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.