Affiliations: [a] Department of Computer Science, BHU, Varanasi, India
| [b] Department of Mathematical Sciences, IIT (BHU), Varanasi, India
| [c] Department of Computer Science and Engineering, IIT (BHU), Varanasi, India
Correspondence:
[*]
Corresponding author. Shivam Shreevastava, Department of Mathematical Sciences, IIT (BHU), Varanasi 221005,
India. E-mail: shivam.rs.apm@itbhu.ac.in.
Abstract: Technological advancement in the area of computing has led to production of huge amount of structured as well as unstructured data. This high dimensional data is very complex to process. Feature selection is one of the widely used techniques for preprocessing of this huge data in predictive analytics. Rough set based feature selection is an approach for handling the vagueness in data and works fine on discrete data but struggles in the continuous case as it requires discretization. This process of discretization leads to information loss. Solution for this problem was given by various authors in form of fuzzy rough set as well as intuitionistic fuzzy rough set based approaches for feature selection. Intuitionistic fuzzy set has certain benefits over the theory of traditional fuzzy sets such as its ability in a better expression of underlying information as well as its aptness to recite fragile ambiguities of the uncertainty of the objective world. The benefits offered by Intuitionistic fuzzy sets is due to the concurrent contemplation of positive, negative and hesitancy degrees for an object to belong to a set. In this paper, three novel approaches of feature reduction based on intuitionistic fuzzy rough set are presented. For this, a new intuitionistic fuzzy rough set model is established by defining a pair of lower and upper approximations. Furthermore, three new approaches of feature selection based on the degree of dependency by using score function, membership grade and cardinality of intuitionistic fuzzy numbers are introduced. Moreover, the basic results on lower and upper approximations based on rough sets are extended for intuitionistic fuzzy rough sets and analogous results are established. Moreover, a suitable algorithm is given based on our proposed approaches. Finally, the proposed algorithm is applied to an arbitrary example data set and comparison has been made with the previous fuzzy rough set based technique. The proposed algorithm is found to be better performing in terms of selected features.
