%0 Conference Paper %1 TinaYu:2001:ACMKDD %A Yu, Tina %A Rutherford, Jim %B The Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining %C San Francisco, California, USA %D 2001 %K Data Data, Dimensionality, High Modeling, Sparse Testing Virtual algorithms, genetic programming, %T Modeling Sparse Engine Test Data Using Genetic programming %U http://www.acm.org/sigs/sigkdd/kdd2001/ %X We demonstrate the generation of an engine test model using Genetic Programming. In particular, a two-phase modeling process is proposed to handle the high-dimensionality and sparseness natures of the engine test data. The resulting model gives high accuracy prediction on training data. It is also very good in predicting low range data values. However, at least partly due to limitations of the data set, its accuracy on validation data and high range data values is not satisfactory. Moreover, the subject experts could not interpret its real-world meaning. We hope the results of this study can benefit other engine oil modeling applications.

@inproceedings{TinaYu:2001:ACMKDD, abstract = {We demonstrate the generation of an engine test model using Genetic Programming. In particular, a two-phase modeling process is proposed to handle the high-dimensionality and sparseness natures of the engine test data. The resulting model gives high accuracy prediction on training data. It is also very good in predicting low range data values. However, at least partly due to limitations of the data set, its accuracy on validation data and high range data values is not satisfactory. Moreover, the subject experts could not interpret its real-world meaning. We hope the results of this study can benefit other engine oil modeling applications.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {San Francisco, California, USA}, author = {Yu, Tina and Rutherford, Jim}, biburl = {https://www.bibsonomy.org/bibtex/2e19f848074e2e5902bcac402e3eeb639/brazovayeye}, booktitle = {The Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, interhash = {3649cd6f1b9c1e473da6c567c12498ba}, intrahash = {e19f848074e2e5902bcac402e3eeb639}, keywords = {Data Data, Dimensionality, High Modeling, Sparse Testing Virtual algorithms, genetic programming,}, month = {26-29 August}, timestamp = {2008-06-19T17:54:59.000+0200}, title = {Modeling Sparse Engine Test Data Using Genetic programming}, url = {http://www.acm.org/sigs/sigkdd/kdd2001/}, year = 2001 }