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Dealing with the evaluation of supervised classification algorithms
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DOI：10.1007/s10462-015-9433-y.png)
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Abstract
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Performance assessment of a learning method related to its prediction ability on independent data is extremely important in supervised classification. This process provides the information to evaluate the quality of a classification model and to choose the most appropriate technique to solve the specific supervised classification problem at hand. This paper aims to review the most important aspects of the evaluation process of supervised classification algorithms. Thus the overall evaluation process is put in perspective to lead the reader to a deep understanding of it. Additionally, different recommendations about their use and limitations as well as a critical view of the reviewed methods are presented according to the specific characteristics of the supervised classification problem scenario.
Keywords:
Supervised classification
Classifier evaluation
Quality measures
Estimation methods
Classification algorithms comparison
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