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Active improvement of hierarchical object features under budget constraints

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Abstract

When we think of an object in a supervised learning setting, we usually perceive it as a collection of fixed attribute values. Although this setting may be suited well for many classification tasks, we propose a new object representation and therewith a new challenge in data mining; an object is no longer described by one set of attributes but is represented in a hierarchy of attribute sets in different levels of quality. Obtaining a more detailed representation of an object comes with a cost. This raises the interesting question of which objects we want to enhance under a given budget and cost model. This new setting is very useful whenever resources like computing power, memory or time are limited. We propose a new active adaptive algorithm (AAA) to improve objects in an iterative fashion. We demonstrate how to create a hierarchical object representation and prove the effectiveness of our new selection algorithm on these datasets.

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Authors and Affiliations

  1. Multimedia Computing Lab, University of Augsburg, 86159, Augsburg, Germany

    Nicolas Cebron

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  1. Nicolas Cebron
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Correspondence to Nicolas Cebron.

Additional information

Nicolas Cebron obtained his diploma in Computer Science from Ostfalia University of Applied Sciences in Braunschweig, Germany, and his PhD in Computer Science from the University of Konstanz, Germany. He spent one year as a postdoctoral researcher in the European Union research project “Bisociation Networks for Creative Information Discovery” and one year as a postdoctoral researcher at the International Computer Science Institute at the University of California, Berkeley. He has received grants from the German Research Foundation and from the German Academic Exchange Service. Nicolas is currently working as a researcher and lecturer in the field of active machine learning and image classification at the University of Augsburg, Germany.

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Cebron, N. Active improvement of hierarchical object features under budget constraints. Front. Comput. Sci. 6, 143–153 (2012). https://doi.org/10.1007/s11704-012-2857-5

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