Indiscernibility criterion based on rough sets in feature selection and detection of landmines

Metal detectors currently used by the teams engaged in decontamination of mines, cannot differentiate a mine from metallic debris where the soil contains large quantities of metal scraps and cartridge cases. Landmines are a significant barrier to financial, economic and social development in various parts of this world, so a sensor is required that reliably confirms that the ground being tested does not contain an explosive device, with almost perfect reliability. Human experts are unable to give belief and plausibility to the rules devised from the huge databases. Rough sets can be applied to classify the landmine data because here any prior knowledge of rules is not required, these rules are automatically discovered from the database. Finally, the whole database is divided into mutually exclusive elementary sets. The rough logic classifier uses lower and upper approximations for determining the class of the objects. The paper aims to induce low-dimensionality rule sets from historical descriptions of domain features which are often of high dimensionality. Moreover, algorithms based on the rough set theory are particularly suited for parallel processing.