Meta-learning based classifier ensemble strategy and its application

A novel meta-learning based classifier ensemble model was presented. Four classifiers i. e. Generalized Winnow, support vector machine, conditional random fields, and maximum entropy were combined using two different meta-learning strategies. Various evidential features specified for the application of biomedical named entity recognition were incorporated into the system to help improve recognition performance. Experimental results show that the classifier ensemble strategy based on meta-learning is obviously superior to the individual classifier based method and superior to the arbitration rule based ensemble method.