A knowledge-based approach to hierarchical classification: A voting metaphor

The paper proposes a new approach to hierarchical classification based on condition-action rules that represent expert knowledge in a given domain. The approach adopts a voting metaphor: each rule is regarded as a voter that expresses a preference for a given category to be assigned to an item to be classified; the category that receives more votes wins. Novel performance measures of hierarchical classifiers are also introduced that aim at overcoming the limitations of the current concepts of precision and recall. The proposed approach can be applied to any hierarchical classification task, for which expert knowledge is available. The viability of the approach and its performance are shown through a real-size application concerning the e-mail dispatching task inside a large public administration. The results obtained demonstrate that the proposed knowledge-based approach to hierarchical classification can reach a performance level comparable to that of human experts, if not even better.