Abstract
Rough set reducts are irreducible attribute subsets preserving discernibility information of a decision system. Computing all reducts has exponential complexity regarding the number of attributes in the decision system. Given the high computational cost of this task, computing only the reducts of minimum length (the shortest reducts) becomes relevant for a wide range of applications. Two recent algorithms have been reported, almost simultaneously, for computing these irreducible attribute subsets with minimum length: MiLIT and MinReduct. MiLIT was designed at the top of the Testor Theory while MinReduct comes from the Rough Set Theory. Thus, in this paper, we present a comparative study of these algorithms in terms of asymptotic complexity and runtime performance.
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Rodríguez-Diez, V., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Lazo-Cortés, M.S., Olvera-López, J.A. (2021). A Comparative Study of Two Algorithms for Computing the Shortest Reducts: MiLIT and MinReduct. In: Roman-Rangel, E., Kuri-Morales, Á.F., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2021. Lecture Notes in Computer Science(), vol 12725. Springer, Cham. https://doi.org/10.1007/978-3-030-77004-4_6
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