Abstract: This chapter is rooted in the ensemble framework and shows how order statistics can be used in the design of a "meta-learner" that examines the outputs of multiple distributed classifers and provides a final decision. Order statistics is one of the key tools of robust statistics, tailored to handling data with outliers. in a distributed data mining scenario in which there is wide variability among the individual classifers because of the underlying quality of the local data that they examine, a meta-learner should be able to tolerate a few outlier classifer results. The robust properties of order statistics based approaches such as median filtering and m-estimators (Arnold, Balakrishnan, and Nagaraja 1992), have been observed in many disciplines. Thus they are an obvious candidate for meta-learning in such environments. ANNOTATION: Reprint: Robust Order Statistics Based Ensembles for Distributed Data Mining
| Limitations: |
 APPROVED FOR PUBLIC RELEASE |
| Pages: |
27 |
| Report Date: |
2001 |
| Contract Number: |
DAAG55-98-0-0230, DAAD19-99-1- |
| Report Number: |
A458593 |
|
|
|
|
Keywords relating to this report:
