An Adaptive Ensemble Approach for Multi-level Semantic Knowledge Representation

摘要

To represent multi-level semantic knowledge implicated in the relationship between feature sets, in this paper we adopt correlation analysis of feature selection methods as a guideline of the separation of features. By redefining information gain to decide which representation is appropriate for a specific word, different ensembles of classifiers are adaptively generated by fitting the validation data globally with different degrees. The test data are then classified by the generated specific ensemble. The final decision is made by taking into consideration both the ability of each ensemble to fit the validation data locally and reducing the risk of over-fitting.