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...
University of Technology, Sydney. Faculty of Engineering and Information Technology.Cost-sensitive l...
International audienceThis paper addresses the problem of learning a multiclass classification syste...
Objectives: The imbalanced bank direct marketing data set utilized in this study is a two-class data...
In practical situations almost all classification problems are cost-sensitive or utility based one w...
Cost-sensitive classification is one of mainstream research topics in data mining and machine learni...
Classification is a data mining technique which is utilized to predict the future by using available...
Graduation date: 2002Many approaches for achieving intelligent behavior of automated (computer) syst...
This thesis studies the cost sensitive learning algorithms that calculate the class learning algorit...
It is an actual and challenging issue to learn cost-sensitive models from those datasets that are wi...
The evaluation of classifier performance in a cost-sensitive setting is straightforward if the opera...
There is a significant body of research in machine learning addressing techniques for performing cla...
Many real-world data mining applications need varying cost for different types of classification err...
Classification is a well-studied problem in machine learning and data mining. Classifier performance...
PurposeThis paper aims to describe the use of a meta-learning framework for recommending cost-sensit...
This paper addresses cost-sensitive classification in the setting where there are costs for measurin...
University of Technology, Sydney. Faculty of Engineering and Information Technology.Cost-sensitive l...
International audienceThis paper addresses the problem of learning a multiclass classification syste...
Objectives: The imbalanced bank direct marketing data set utilized in this study is a two-class data...
In practical situations almost all classification problems are cost-sensitive or utility based one w...
Cost-sensitive classification is one of mainstream research topics in data mining and machine learni...
Classification is a data mining technique which is utilized to predict the future by using available...
Graduation date: 2002Many approaches for achieving intelligent behavior of automated (computer) syst...
This thesis studies the cost sensitive learning algorithms that calculate the class learning algorit...
It is an actual and challenging issue to learn cost-sensitive models from those datasets that are wi...
The evaluation of classifier performance in a cost-sensitive setting is straightforward if the opera...
There is a significant body of research in machine learning addressing techniques for performing cla...
Many real-world data mining applications need varying cost for different types of classification err...
Classification is a well-studied problem in machine learning and data mining. Classifier performance...
PurposeThis paper aims to describe the use of a meta-learning framework for recommending cost-sensit...
This paper addresses cost-sensitive classification in the setting where there are costs for measurin...
University of Technology, Sydney. Faculty of Engineering and Information Technology.Cost-sensitive l...
International audienceThis paper addresses the problem of learning a multiclass classification syste...
Objectives: The imbalanced bank direct marketing data set utilized in this study is a two-class data...