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Hybrid rough-bijective soft set classification system

Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In today’s medical world, the patient’s data with symptoms and diseases are expanding rapidly, so that analysis of all factors with updated knowledge about symptoms and corresponding new treatment is merely not possible by medical experts. Hence, the essential for an intelligent system to reflect the different issues and recognize an appropriate model between the different parameters is evident. In recent decades, rough set theory (RST) has been broadly applied in various fields such as medicine, business, education, engineering and multimedia. In this study, a hybrid intelligent system that combines rough set (RST) and bijective soft set theory (BISO) to build a robust classifier model is proposed. The aim of the hybrid system is to exploit the advantages of the constituent components while eliminating their limitations. The resulting approach is thus able to handle data inconsistency in datasets through rough sets, while obtaining high classification accuracy based on prediction using bijective soft sets. Toward estimating the performance of the hybrid rough-bijective soft set (RBISO)-based classification approach, six benchmark medical datasets (Wisconsin breast cancer, liver disorder, hepatitis, Pima Indian diabetes, echocardiogram data and thyroid gland) from the UCI repository of machine learning databases are utilized. Experimental results, based on evaluation in terms of sensitivity, specificity and accuracy, are compared with other well-known classification methods, and the proposed algorithm provides an effective method for medical data classification.

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Abbreviations

\(C\) :

A set of condition features

\(D\) :

A decision features set

\(U\) :

A nonempty and finite set of objects (universe)

\(B\) :

Equivalence relations of features

\(IND\left( B \right)\) :

B-indiscernibility relation

\(\left( {x,y} \right) \in IND\left( B \right)\) :

Objects x and y are indiscernible from each other by attributes from B

\(\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{B} X\) :

Lower approximation

\(\bar{B}X\) :

Upper approximation

\(\left[ x \right]_{B}\) :

The equivalence classes of the B-indiscernibility relation

\(BND\left( D \right)\) :

B-boundary region of D

\(P\) :

Set of parameters

\(M\) :

M is a mapping of P into the set of all subsets of the set U

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Inbarani, H.H., Kumar, S.U., Azar, A.T. et al. Hybrid rough-bijective soft set classification system. Neural Comput & Applic 29, 67–78 (2018). https://doi.org/10.1007/s00521-016-2711-z

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