The uncertainty in the environment typically generates noisy concept alternatives and leads to an overpopulated concept lattice. From a computational point of view, a straightforward filtering of the noisy concept lattice will suffer from an exponential-size computational overkill, and from a semantical one [ will face numerous ambiguities due to an overfitting. We managed to bypass the filtering problem by applying a sort of probabilistic approach. We developed a probabilistic generaliza-tion of formal concepts which seems to avoid a monstrous combinatorial complexity of a complete context lattice construction. The theoretical base for this method is described, as well as a ready-to-work noise resis-tant algorithm. The algorithm has been tested and showed a moderate precision and recall rate on various datasets, including a toy one pre-sented with the presence of a 2, 3 or 5% random noise.
|Журнал||CEUR Workshop Proceedings|
|Состояние||Опубликовано - 2016|
|Событие||2nd International Workshop on Soft Computing Applications and Knowledge Discovery, SCAKD 2016 - Moscow, Российская Федерация|
Продолжительность: 18 июл 2016 → …