Paper Info
Title
Indiscernibility Criterion Based On Rough Sets In Feature Selection And Detection Of Landmines
Abstract
Metal detectors currently used by the teams engaged in decontamination of mining, and 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 will reliably confirm 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.
Year
DOI
Venue
2005
10.1109/GRC.2005.1547324
2005 IEEE International Conference on Granular Computing, Vols 1 and 2
Keywords
Field
DocType
indiscernibility, lower and upper approximations, rough sets, soft computing
Data mining,Feature selection,Computer science,Explosive material,Artificial intelligence,Soft computing,Classifier (linguistics),Pattern recognition,Curse of dimensionality,Feature extraction,Rough set,Explosive device,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
3
Authors
2
Name
Order
Citations
PageRank
Ajay Choudhari100.68
G. C. Nandi27110.28