Proceedings ArticleDOI
On Covering Based Pessimistic Multigranular Rough Sets
B. K. Tripathy,K. Govinda Rajulu +1 more
- pp 708-713
TLDR
The notion of Covering Based Pessimistic Multigranular Rough Sets (CBPMGRS) is introduced and some of the properties of basic rough sets, which were not true for CBOMGRS, are true for this paper and seems to be more natural extensions of the basic concepts.Abstract:
Rough sets were introduced by Pawlak as a model to capture impreciseness in data and several techniques in different directions have been developed to perform operations on such data through the model. But the basic model of rough set introduced is unigranular from the granular computing point of view. So, attempts have been made to introduce multigranulation using rough sets. Two models called optimistic multigranular rough sets and pessimistic multigranular rough sets were introduce by Qian et al in 2006 and 2010 respectively. However, like the basic case, the information granules generated were introduced by equivalence relations and as a result the granules are members of partitions induced on the universe. However, such concepts have limited applications due to the rarity of equivalence relations or equivalently partitions of universes. The notion of cover is more general than partition and covers are available in abundance in real life situations. So, even in the case of basic rough sets the definition has been extended to develop covering based rough sets. Recently, the concept of multigranularity has been extended by Liu et al to introduce four types of covering based optimistic multigranular rough sets (CBOMGRS). In this paper we introduce the notion of Covering Based Pessimistic Multigranular Rough Sets (CBPMGRS) and proved some properties. The important observation is that some of the properties of basic rough sets, which were not true for CBOMGRS, are true for CBPMGRS. So, CBPMGRS seems to be more natural extensions of the basic concepts than CBOMGRS.read more
Citations
More filters
Journal ArticleDOI
A Fault Prediction Algorithm Based on Rough Sets and Back Propagation Neural Network for Vehicular Networks
TL;DR: The fault prediction algorithm is simulated and analyzed using NS-2 and MATLAB, respectively, and the results show that the proposed algorithm can accurately diagnose and predict faults using the predicted data.
Journal ArticleDOI
Properties of Multigranular Rough Sets on Fuzzy Approximation Spaces and their Application to Rainfall Prediction
B. K. Tripathy,Urmi Bhambhani +1 more
TL;DR: Two types of multigranular rough sets on fuzzy approximation spaces (optimistic and pessimistic) are introduced, several of their properties are studied and how this notion can be used for prediction of rainfall are illustrated.
Proceedings ArticleDOI
Unsupervised Fuzzy eIX: Evolving Internal-eXternal Fuzzy Clustering
Charles Aguiar,Daniel Leite +1 more
TL;DR: A new unsupervised learning method for numerical data called evolving Internal-eXternal Fuzzy clustering method (Fuzzy eIX), which could keep up with its accuracy in a scenario in which offline-trained classifiers would clearly have their accuracy drastically dropped.
Journal Article
An integrated covering-based rough fuzzy set clustering approach for sequential data
P. Prabhavathy,B. K. Tripathy +1 more
TL;DR: Covering-based rough fuzzy set clustering approach is proposed to resolve the uncertainty of sequence data and uses covering-based similarity measure which gives better results as compared to rough set which uses set and sequence similarity measure.
Book ChapterDOI
Some More Properties of Covering Based Pessimistic Multigranular Rough Sets
B. K. Tripathy,K. Govindarajulu +1 more
TL;DR: The results in this paper suggest that CBPMGRS seems to be more natural extension of the basic concept than CBOMGRS, which is based on the notion of Covering Based Pessimistic Multigranular Rough Sets.
References
More filters
Journal ArticleDOI
Rough sets
TL;DR: This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
Journal ArticleDOI
Rough sets: Some extensions
Zdzisław Pawlak,Andrzej Skowron +1 more
TL;DR: Some extensions of the rough set approach are presented and a challenge for the roughSet based research is outlined and it is outlined that the current rough set based research paradigms are unsustainable.
Proceedings ArticleDOI
Rough Set Method Based on Multi-Granulations
Yuhua Qian,Jiye Liang +1 more
TL;DR: It is shown that some properties of Pawlak rough set are special instances of MGRS, and approximation measure of set described by using multi-granulations is always better than by using single granulation, which is suitable for describing more accurately the concept and solving problem according to user requirement.
Journal ArticleDOI
On multi-granulation covering rough sets
Caihui Liu,Duoqian Miao,Jin Qian +2 more
TL;DR: It is found that for any subset X ⊆ U, the lower approximations of X and the upper approximation of X under the four types of MGCRS models can construct a lattice, if the authors consider the binary relation of inclusion.
Related Papers (5)
A Comparative Analysis of Multigranular Approaches and on Topoligical Properties of Incomplete Pessimistic Multigranular Rough Fuzzy Sets
B. K. Tripathy,M. Nagaraju +1 more
On Approximate Equivalences of Multigranular Rough Sets and Approximate Reasoning
B. K. Tripathy,Anirban Mitra +1 more