Name
Abstract | ||
|---|---|---|
Material corrosion has caused more and more losses and costs these years, so the world begin to pay much attention to this problem. In this paper, we mainly discuss the modeling and feature selection of Material corrosion data. With our experimental data with very small sample size, a model of corrosion rate is built. After specialized data preprocessing. By combining RFE and SVM, a novel feature selection method SVM-RFE is introduced. Then integrating this feature selection method and SVM modeling method, a special modeling framework is built. According to the experiments, the priority of this method is established not only on algorithm efficiency but also on predicting precision. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TSP.2011.6043693 | TSP |
Keywords | Field | DocType |
corrosion modeling,data processing feature selection,materials science computing,environmental science computing,svm-rfe,corrosion,support vector machine,feature selection,material corrosion modelling,support vector machines,data processing,data preprocessing,materials,data model,classification algorithms,data models,training data,atmospheric modeling | Data modeling,Data mining,Algorithmic efficiency,Feature selection,Pattern recognition,Experimental data,Corrosion,Computer science,Support vector machine,Data pre-processing,Artificial intelligence,Statistical classification | Conference |
Volume | Issue | ISBN |
null | null | 978-1-4577-1410-8 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xintao Qiu | 1 | 0 | 1.01 |
| Fu Dongmei | 2 | 4 | 3.08 |
| Zhenduo Fu | 3 | 0 | 0.68 |
| Kamil Ríha | 4 | 23 | 13.58 |
| Radim Burget | 5 | 76 | 25.45 |