Combining Boosted Trees with Metafeature Engineering for Predictive Maintenance

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Cerqueira, V (Author) Other; Pinto, F (Author) Other; Sa, C (Author) Other; Carlos Soares (Author) FEUP

Conference proceedings International

Title: ADVANCES IN INTELLIGENT DATA ANALYSIS XV

Pages: 393-397

15th International Symposium on Intelligent Data Analysis (IDA)

Stockholm Univ, Dept Comp & Syst Sci, Stockholm, SWEDEN, OCT 13-15, 2016

Indexing

ISI Web of Knowledge - 16 Citations

Scopus - 17 Citations

Other information

Authenticus ID: P-00K-WFP

DOI: 10.1007/978-3-319-46349-0_35

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5