Ranking-based rule classifier optimisation

Ranking is a strategy widely used for estimating relevance or importance of available characteristic features. Depending on the applied methodology, variables are assessed individually or as subsets, by some statistics referring to information theory, machine learning algorithms, or specialised procedures that execute systematic search through the feature space. The information about importance of attributes can be used in the pre-processing step of initial data preparation, to remove irrelevant or superfluous elements. It can also be employed in post-processing, for optimisation of already constructed classifiers. The chapter describes research on the latter approach, involving filtering inferred decision rules while exploiting ranking positions and scores of features. The optimised rule classifiers were applied in the domain of stylometric analysis of texts for the task of binary authorship attribution.