Best paper at IDEAL 2015

Data-driven-Hierarchy-Tree
Web genre classification via hierarchical multi-label classification Gjorgji Madjarov, Vedrana Vidulin, Ivica Dimitrovski and Dragi Kocev In this paper, the web genres labels are exploited by constructing a hierarchy of web genres and then use methods for hierarchical multi-label classification to boost the predictive performance. Two methods for hierarchy construction: expert-based and data-driven are used.
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Welcome to MAESTRA project

Welcome to MAESTRA project Learning from Massive, Incompletely annotated, and Structured Data The need for machine learning (ML) and data mining (DM) is ever growing due to the increased pervasiveness of data analysis tasks in almost every area of life, including business, science and technology. Not only is the pervasiveness of data analysis tasks increasing, but so is their complexity. We are increasingly often facing predictive modelling tasks involving one or several of the following compl...
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