An Adaptive Hierarchical Model Based on Fusion of Ontology and Context

In documents classification, utilization of ontology and context is proven an effective way to improve text classification, but also presented as a difficult problem. This paper presents a novel adaptive hierarchical classification model(HAC) which is based on concept expansion with ontology. Using the hierarchy structure of the model, an incremental learning algorithm in terms of both feature and category associated contextis presented, and then with these, a more efficient and blocking-immuned hierarchical classification method is proposed. Experimental results showed that better performance can be achieved with less classification time in HAC, which is of particular significance in document online classification.