Information-Based Evaluation Criterion for Classifier's Performance
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Machine Learning
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Information Sciences: an International Journal
Neural network classification of otoneurological data and its visualization
Computers in Biology and Medicine
A scatter method for data and variable importance evaluation
Integrated Computer-Aided Engineering
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Dcision tree induction, as well as other inductive learning methods, requires training data of high quality to be able to generate accurate and reliable classification models. Example cases should form a representative sample from the application area, and the attributes used to describe example cases should be relevant and adequate for the classification task to be solved. In this paper, measures of the strength of association and an entropy-based approach have been used to assess the quality of the training data. Studied classification tasks related to three otological data sets: a conscript data set, a vertigo data set, and a postoperative nausea and vomiting data set. The pape suggests that the studied approaches give some guidelines about the quality of the training data, but other approaches are also needed to guide training data building.