Performance Measures in Classification of Human Communications

This study emphasizes the importance of using appropriate measures in particular text classification settings. We focus on methods that evaluate how well a classifier performs. The effect of transformations on the confusion matrix are considered for eleven well-known and recently introduced classification measures. We analyze the measure's ability to retain its value under changes in a confusion matrix. We discuss benefits from the use of the invariant and non-invariant measures with respect to characteristics of data classes. Key words: Machine Learning, Evaluation Measures, Text Classification, Human Communication.

• Type: Conference
• Year: 2007
• Where: AI
• Authors: Marina Sokolova, Guy Lapalme
• Keywords: AI 2007, Artificial Intelligence, Confusion Matrix, Text Classification, Text Classification Settings