Granular computing: perspectives and challenges
Granular computing, as a new and rapidly growing paradigm of information processing, has
attracted many researchers and practitioners. Granular computing is an umbrella term to …
attracted many researchers and practitioners. Granular computing is an umbrella term to …
Belief functions and rough sets: Survey and new insights
Rough set theory and belief function theory, two popular mathematical frameworks for
uncertainty representation, have been widely applied in different settings and contexts …
uncertainty representation, have been widely applied in different settings and contexts …
Neighborhood rough set based heterogeneous feature subset selection
Q Hu, D Yu, J Liu, C Wu - Information sciences, 2008 - Elsevier
Feature subset selection is viewed as an important preprocessing step for pattern
recognition, machine learning and data mining. Most of researches are focused on dealing …
recognition, machine learning and data mining. Most of researches are focused on dealing …
Feature selection based on neighborhood self-information
The concept of dependency in a neighborhood rough set model is an important evaluation
function for the feature selection. This function considers only the classification information …
function for the feature selection. This function considers only the classification information …
Soft sets and soft rough sets
In this study, we establish an interesting connection between two mathematical approaches
to vagueness: rough sets and soft sets. Soft set theory is utilized, for the first time, to …
to vagueness: rough sets and soft sets. Soft set theory is utilized, for the first time, to …
Relationship between generalized rough sets based on binary relation and covering
W Zhu - Information Sciences, 2009 - Elsevier
Rough set theory is a powerful tool for dealing with uncertainty, granularity, and
incompleteness of knowledge in information systems. This paper systematically studies a …
incompleteness of knowledge in information systems. This paper systematically studies a …
On a novel uncertain soft set model: Z-soft fuzzy rough set model and corresponding decision making methods
In this paper, a kind of novel soft set model called a Z-soft fuzzy rough set is presented by
means of three uncertain models: soft sets, rough sets and fuzzy sets, which is an important …
means of three uncertain models: soft sets, rough sets and fuzzy sets, which is an important …
Intuitionistic fuzzy TOPSIS method based on CVPIFRS models: an application to biomedical problems
In order to obtain the weights of a set of criteria by means of real-world data, an effective
method based on the covering-based variable precision intuitionistic fuzzy rough set …
method based on the covering-based variable precision intuitionistic fuzzy rough set …
Covering-based generalized IF rough sets with applications to multi-attribute decision-making
Multi-attribute decision-making (MADM) can be regarded as a process of selecting the
optimal one from all objects. Traditional MADM problems with intuitionistic fuzzy (IF) …
optimal one from all objects. Traditional MADM problems with intuitionistic fuzzy (IF) …
Fuzzy β-covering based (I, T)-fuzzy rough set models and applications to multi-attribute decision-making
Multi-attribute decision-making (MADM) can be regarded as a process of selecting the
optimal one from all alternatives. Traditional MADM problems with fuzzy information are …
optimal one from all alternatives. Traditional MADM problems with fuzzy information are …