Name
Abstract | ||
|---|---|---|
We describe a data mining workflow for predictive maintenance of the Air Pressure System in heavy trucks. Our approach is composed by four steps: (i) a filter that excludes a subset of features and examples based on the number of missing values (ii) a metafeatures engineering procedure used to create a meta-level features set with the goal of increasing the information on the original data; (iii) a biased sampling method to deal with the class imbalance problem; and (iv) boosted trees to learn the target concept. Results show that the metafeatures engineering and the biased sampling method are critical for improving the performance of the classifier. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46349-0_35 | ADVANCES IN INTELLIGENT DATA ANALYSIS XV |
Keywords | Field | DocType |
Predictive maintenance,Anomaly detection,Boosting,Metalearning | Anomaly detection,Metalearning,Computer science,Sampling bias,Artificial intelligence,Boosting (machine learning),Missing data,Predictive maintenance,Classifier (linguistics),Workflow,Machine learning | Conference |
Volume | ISSN | Citations |
9897 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vítor Cerqueira | 1 | 23 | 4.86 |
| Fábio Pinto | 2 | 15 | 3.57 |
| Cláudio Rebelo de Sá | 3 | 43 | 3.88 |
| Carlos Soares | 4 | 95 | 18.18 |