Non-symmetric similarity relation-based rough
set model (NS-RSM) is viewed as mathematical tool t... more Non-symmetric similarity relation-based rough set model (NS-RSM) is viewed as mathematical tool to deal with the analysis of imprecise and uncertain information in incomplete information systems with “?” values. NS-RSM relies on the concept of non-symmetric similarity relation to group equivalent objects and generate knowledge granules that are then used to approximate the target set. However, NS-RSM results in unpromising approximation space when addressing inconsistent data sets that have lots of boundary objects. This is because objects in the same similarity classes are not necessarily similar to each other and may belong to different target classes. To enhance NS-RSM capability, we introduce the maximal limited similarity-based rough set model (MLS-RSM) which describes the maximal collection of indistinguishable objects that are limited tolerance to each other in similarity classes. This allows accurate computation to be done for the approximation space. Furthermore, approximation accuracy comparisons have been conducted among NS-RSMandMLS-RSM.The results demonstrate thatMLSRSM model outperforms NS-RSM and can approximate the target set more efficiently.
Non-symmetric similarity relation-based rough
set model (NS-RSM) is viewed as mathematical tool t... more Non-symmetric similarity relation-based rough set model (NS-RSM) is viewed as mathematical tool to deal with the analysis of imprecise and uncertain information in incomplete information systems with “?” values. NS-RSM relies on the concept of non-symmetric similarity relation to group equivalent objects and generate knowledge granules that are then used to approximate the target set. However, NS-RSM results in unpromising approximation space when addressing inconsistent data sets that have lots of boundary objects. This is because objects in the same similarity classes are not necessarily similar to each other and may belong to different target classes. To enhance NS-RSM capability, we introduce the maximal limited similarity-based rough set model (MLS-RSM) which describes the maximal collection of indistinguishable objects that are limited tolerance to each other in similarity classes. This allows accurate computation to be done for the approximation space. Furthermore, approximation accuracy comparisons have been conducted among NS-RSMandMLS-RSM.The results demonstrate thatMLSRSM model outperforms NS-RSM and can approximate the target set more efficiently.
Uploads
set model (NS-RSM) is viewed as mathematical tool to deal
with the analysis of imprecise and uncertain information in
incomplete information systems with “?” values. NS-RSM
relies on the concept of non-symmetric similarity relation to
group equivalent objects and generate knowledge granules
that are then used to approximate the target set. However,
NS-RSM results in unpromising approximation space when
addressing inconsistent data sets that have lots of boundary
objects. This is because objects in the same similarity classes
are not necessarily similar to each other and may belong
to different target classes. To enhance NS-RSM capability,
we introduce the maximal limited similarity-based rough set
model (MLS-RSM) which describes the maximal collection
of indistinguishable objects that are limited tolerance to each
other in similarity classes. This allows accurate computation
to be done for the approximation space. Furthermore, approximation
accuracy comparisons have been conducted among
NS-RSMandMLS-RSM.The results demonstrate thatMLSRSM
model outperforms NS-RSM and can approximate the
target set more efficiently.
set model (NS-RSM) is viewed as mathematical tool to deal
with the analysis of imprecise and uncertain information in
incomplete information systems with “?” values. NS-RSM
relies on the concept of non-symmetric similarity relation to
group equivalent objects and generate knowledge granules
that are then used to approximate the target set. However,
NS-RSM results in unpromising approximation space when
addressing inconsistent data sets that have lots of boundary
objects. This is because objects in the same similarity classes
are not necessarily similar to each other and may belong
to different target classes. To enhance NS-RSM capability,
we introduce the maximal limited similarity-based rough set
model (MLS-RSM) which describes the maximal collection
of indistinguishable objects that are limited tolerance to each
other in similarity classes. This allows accurate computation
to be done for the approximation space. Furthermore, approximation
accuracy comparisons have been conducted among
NS-RSMandMLS-RSM.The results demonstrate thatMLSRSM
model outperforms NS-RSM and can approximate the
target set more efficiently.