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Rough set-based dimensionality reduction for supervised and unsupervised learning

Qiang ShenAlexios Chouchoulas — 2001

International Journal of Applied Mathematics and Computer Science

The curse of dimensionality is a damning factor for numerous potentially powerful machine learning techniques. Widely approved and otherwise elegant methodologies used for a number of different tasks ranging from classification to function approximation exhibit relatively high computational complexity with respect to dimensionality. This limits severely the applicability of such techniques to real world problems. Rough set theory is a formal methodology that can be employed to reduce the dimensionality...

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