This paper describes how to make use of the cost information related to the extraction of each feature in a feature selection algorithm. For instance, in medical diagnosis, the different tests a patient might take during the diagnosis process can have different associated costs. The main idea is to change the feature selection framework in order to get low-cost subsets of informative features. This work proposes a way to introduce this information in a well-known machine learning algorithm, the Set Covering Machine.
|Title of host publication||Proceedings - 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010|
|Number of pages||7|
|Publication status||Published - 2010|
|Event||10th IEEE International Conference on Data Mining Workshops, ICDMW 2010 - Sydney, NSW, Australia|
Duration: 14 Dec 2010 → 17 Dec 2010
|Name||Proceedings - IEEE International Conference on Data Mining, ICDM|
|Conference||10th IEEE International Conference on Data Mining Workshops, ICDMW 2010|
|Period||14/12/10 → 17/12/10|
Copyright 2011 Elsevier B.V., All rights reserved.
- Cost-sensitive learning
- Feature selection
- Set covering machine