Abstract
We propose a unifying FCA-based framework for some questions in data analysis and data mining, combining ideas from Rough Set Theory, JSM-reasoning, and feature selection in machine learning. Unlike the standard rough set model the indiscernibility relation in our paper is based on a quasi-order, not necessarily an equivalence relation. Feature selection, though algorithmically difficult in general, appears to be easier in many cases of scaled many-valued contexts, because the difficulties can at least partially be projected to the scale contexts. We propose a heuristic algorithm for this.
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Ganter, B., Kuznetsov, S.O. (2008). Scale Coarsening as Feature Selection. In: Medina, R., Obiedkov, S. (eds) Formal Concept Analysis. ICFCA 2008. Lecture Notes in Computer Science(), vol 4933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78137-0_16
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DOI: https://doi.org/10.1007/978-3-540-78137-0_16
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