Hierarchical Classification With Bayesian Networks and Chained Classifiers

In this work is proposed a method for Hierarchical Classification, which takes advantage of the hierarchical structure to influence the prediction of local classifiers with their neighbors. To achieve this, two strategies are combined. The first is to represent the hierarchical structure as a Bayesian network, and the second is to build chained classifiers that feed the Bayesian network as local classifiers. The proposed method was tested in several datasets of functional genomics, which consist of tree-structured hierarchies. The results of several variants of the proposed method are compared to the standard methods, Flat and Top-Down, as well as with a start of the art technique, showing superior performance under several metrics.