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RETRACTED ARTICLE: Hybrid rough-bijective soft set classification system

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This article wasretracted on 21 May 2024

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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

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

Objectsx andy are indiscernible from each other by attributes fromB

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

Lower approximation

\(\bar{B}X\) :

Upper approximation

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

The equivalence classes of theB-indiscernibility relation

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

B-boundary region ofD

\(P\) :

Set of parameters

\(M\) :

M is a mapping ofP into the set of all subsets of the setU

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Author information

Authors and Affiliations

  1. Department of Computer Science, Periyar University, Salem, India

    H. Hannah Inbarani & S. Udhaya Kumar

  2. Faculty of Computers and Information, Benha University; Nanoelectronics Integrated Systems Center (NISC), Nile University, Benha; 6th of October City, Egypt

    Ahmad Taher Azar

  3. Faculty of Computer and Information, Cairo University, Scientific Research Group in Egypt, Cairo, Egypt

    Aboul Ella Hassanien

Authors
  1. H. Hannah Inbarani

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  2. S. Udhaya Kumar

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  3. Ahmad Taher Azar

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  4. Aboul Ella Hassanien

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Corresponding author

Correspondence toS. Udhaya Kumar.

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Inbarani, H.H., Kumar, S.U., Azar, A.T.et al. RETRACTED ARTICLE: Hybrid rough-bijective soft set classification system.Neural Comput & Applic29, 67–78 (2018). https://doi.org/10.1007/s00521-016-2711-z

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