Abstract
In practical situations almost all classification problems are cost-sensitive or utility based one way or another. This exercise mimics a real situation in which students first have to translate a description into a datamining workflow, learn a prediction model, apply it to new data, and set up a testing strategy to estimate what will be the performance. The exercise is suitable for students following an introductory data mining course; it has been used in my introductory data mining class (3ECTS; 3rd BSc Computer Science students) for two years now. Students work on it in class for approximately 1 hour and finish the exercise at home. Solutions are to be sent to the lecturer and discussion the solutions the next lecture takes approximately 30 minutes.
| Original language | English |
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| Pages | 1-6 |
| Publication status | Published - 2012 |
| Event | conference; Workshop on Teaching Machine Learning; 2012-06-30; 2012-06-30 - Duration: 30 Jun 2012 → 30 Jun 2012 |
Conference
| Conference | conference; Workshop on Teaching Machine Learning; 2012-06-30; 2012-06-30 |
|---|---|
| Period | 30/06/12 → 30/06/12 |
| Other | Workshop on Teaching Machine Learning |
Bibliographical note
Workshop on Teaching Machine Learning (Edinburgh, Scotland, June 30, 2012; co-located with ICML 2012)Fingerprint Dive into the research topics of 'Cost-sensitive classification problem (Poster)'. Together they form a unique fingerprint.
Cite this
Calders, T. G. K., & Pechenizkiy, M. (2012). Cost-sensitive classification problem (Poster). 1-6. Poster session presented at conference; Workshop on Teaching Machine Learning; 2012-06-30; 2012-06-30, .