The idea of ensemble methodology is to build a predictive model by integrating multiple models. It is well-known that ensemble methods can be used for improving prediction performance. In this chapter we provide an overview of ensemble methods in classification tasks. We present all important types of ensemble method including boosting and bagging. Combining methods and modeling issues such as ensemble diversity and ensemble size are discussed.
|Title of host publication||The Data Mining and Knowledge Discovery Handbook|
|Editors||Lior Rokach , O. Maimon|
|Publisher||Springer, Boston, MA|
|Number of pages||24|
|State||Published - 2005|