Constructing classifier committee exploiting transformations of attribute domains

Reaching a decision through deliberations of some component decision-making units is the notion applied in many domains. Collective judgement, combining information coming from many sources, with diverse properties or based on various data, can lead to enriched knowledge and result in more accurate predictions and enhanced understanding of the domain. The paper describes the research in which the constructed classifier committees used one type of learner and relied on the input data transformed by different discretisation processes. Based on partially distributed data and continuous and discrete representations of available attributes, several defined voting scenarios were investigated. They were employed for two well-known inducers applied to the task of authorship attribution in the domain of stylometric analysis of texts. The results from the performed experiments enable to observe conditions where decision aggregation based on characterising property of supervised discretisation algorithms can be advantageous, providing insights on roles played by the attributes in the decision-making process and the impact of data transformation.