In real-world applications, the data gathering process is necessarily bounded by costs in terms of money, time or resources that need to be spent in order to sample a sufficient amount of good quality data. From this point of view Feature Selection (FS) is essential to reduce the total sampling cost while trying to keep the information content of sampled data unaltered, and Rough Sets (RS) offer a natural representation of FS in terms of the so-called reducts. In this paper a modified version of the Quick Reduct (QR) algorithm is proposed, where the criterium to add features to the reduct accounts also for the costs of the features. Exploiting granular computing and the indiscernibility principle, the Test-Cost-Sensitive Quick Reduct (TCSQR) here proposed efficiently derives a close-to-optimal subset of informative and inexpensive features. Promising experimental results have been obtained on three different cost scenarios.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.