Name
Abstract | ||
|---|---|---|
Railway operations are inherently complex and source of several problems. In particular, track geometry defects are one of the leading causes of train accidents in the United States. This paper presents a solution approach which entails the construction of an ensemble classifier to forecast the degradation of track geometry. Our classifier is constructed by solving the problem from three different perspectives: deterioration, regression and classification. We considered a different model from each perspective and our results show that using an ensemble method improves the predictive performance. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.ress.2016.12.012 | Reliability Engineering & System Safety |
Keywords | Field | DocType |
Railroad maintenance,Defects,Gamma process,Logistic regression,Support vector machines,Classification,Ensemble algorithms | Data mining,Regression,Gamma process,Support vector machine,Artificial intelligence,Engineering,Track geometry,Classifier (linguistics),Logistic regression,Ensemble learning,Machine learning | Journal |
Volume | ISSN | Citations |
161 | 0951-8320 | 2 |
PageRank | References | Authors |
0.41 | 14 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Iván Cárdenas-Gallo | 1 | 2 | 0.41 |
| Carlos A. Sarmiento | 2 | 2 | 0.41 |
| Gilberto A. Morales | 3 | 2 | 0.74 |
| Manuel A. Bolívar | 4 | 6 | 1.17 |
| Raha Akhavan-Tabatabaei | 5 | 66 | 11.78 |