Multi-dimensional Classification Automation with Human Interface Based on Metromaps

There are several existing multidimensional classification methods which attempt to build meaningful multidimensional structures from simple Binary Relevance (BR) classifiers. One of the recent methods is the Classifier Chains (CC) method which applies several binary classes in sequence. While the method offers a major reduction in complexity it is not clear how to define the order of binary classes in the chain. This paper presents a novel method called Metro Map Classifier (MMC) in which binary classes are connected by a metro map and can be applied in any order. Results show that MMCs perform between 10% and 50% better depending on the type of content. The ultimate target of this research is automation when selecting a small subset of content from Big Data.