document: A general approach to incorporate data quality matrices into data mining algorithms Data quality is a central issue for many information-oriented organizations . Recent advances in the data quality field reflect the view that a database is the product of a manufacturing process . While routine errors , such as non-existent zip codes , can be detected and corrected using traditional data cleansing tools , many errors systemic to the manufacturing process can not be addressed . Therefore , the product of the data manufacturing process is an imprecise recording of information about the entities of interest ( i.e. customers , transactions or assets ) . In this way , the database is only one ( flawed ) version of the entities it is supposed to represent . Quality assurance systems such as Motorola 's Six-Sigma and other continuous improvement methods document the data manufacturing process 's shortcomings . A widespread method of documentation is quality matrices . In this paper , we explore the use of the readily available data quality matrices for the data mining classification task . We first illustrate that if we do not factor in these quality matrices , then our results for prediction are sub-optimal . We then suggest a general-purpose ensemble approach that perturbs the data according to these quality matrices to improve the predictive accuracy and show the improvement is due to a reduction in variance .
doc_bio_tags: O O O O O B I O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O