Semantic similarity-based approach to enhance supervised classification learning accuracy
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This brief communication discusses the usefulness of semantic similarity measures for the evaluation and amelioration of the accuracy of supervised classification learning. It proposes a semantic similarity-based method to enhance the choice of adequate labels for the classification algorithm as well as two metrics (SS-Score and TD-Score) and a curve (SA-Curve) that can be coupled to statistical evaluation measures of supervised classification learning to take into consideration the impact of the semantic aspect of the labels on the classification accuracy.
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