Optimistic Multi-granulation Rough Set Based Classification for Medical Diagnosis

Kumar, S. Senthil; Inbarani, H. Hannah

doi:10.1016/j.procs.2015.03.219

Cited by 45 publications

(17 citation statements)

References 17 publications

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“…Intelligent tools that are usually being utilized for medicinal determination incorporate rough set-based strategies. As rough set-based classification strategies have some points of interest, for example, simple adjustment to various sorts of information and information structures, and great speculation abilities, it has been effectively utilized as a part of numerous medical applications including pattern classification [46][47][48] and decision-making.…”

Section: Problem Statementmentioning

confidence: 99%

“…In this paper, seven pattern categorization validation performance indicators were evaluated. There are precision, sensitivity, F-measure (Czekanowski-Dice index), Folke-Mallows index, Kulczynski index, Rand index and Russel-Rao indexes which were applied for assessing the precision of categorization [9,46,47]. Precision, sensitivity and Czekanowski-Dice index are universal validation dealings in categorization examination, and remaining four are local validation performance indicators in categorization learning.…”

Section: Experimental Analysismentioning

confidence: 99%

“…It is multi-mapping items for inadequate information about the genuine characteristic [18][19][20][21][22][23][24][25][26][27][28]. Hybrid rough set systems have been used in different applications for feature selections [29][30][31][32][33][34], classifications [35][36][37][38][46][47][48] and image segmentation [39]. Jaganathan et al [40] suggested amount of feature significance based on fuzzy entropy, experienced with a radial basis function network classifier for classification using five UCI healthcare benchmark data sets.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED ARTICLE: Covering-based rough set classification system

Kumar

Inbarani

Azar

et al. 2016

Neural Comput & Applic

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Medical data classification is applied in intelligent medical decision support system to classify diseases into different categories. Several classification methods are commonly used in various healthcare settings. These techniques are fit for enhancing the nature of prediction, initial identification of sicknesses and disease classification. The categorization complexities in healthcare area are focused around the consequence of healthcare data investigation or depiction of medicine by the healthcare professions. This study concentrates on applying uncertainty (i.e. rough set)-based pattern classification techniques for UCI healthcare data for the diagnosis of diseases from different patients. In this study, covering-based rough set classification (i.e. proposed pattern classification approach) is applied for UCI healthcare data. Proposed CRS gives effective results than delicate pattern classifier model. The results of applying the CRS classification method to UCI healthcare data analysis are based upon a variety of disease diagnoses. The execution of the proposed covering-based rough set classification is contrasted with other approaches, such as rough set (RS)-based classification methods, Kth nearest neighbour, improved bijective soft set, support vector machine, modified soft rough set and back propagation neural network methodologies using different evaluating measures.

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Section: Problem Statementmentioning

confidence: 99%

Section: Experimental Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RETRACTED ARTICLE: Covering-based rough set classification system

Kumar

Inbarani

Azar

et al. 2016

Neural Comput & Applic

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“…Yao et al [21] studied the rough set models under the multi-granulation approximation space. Now the MRS model has been used widely and has produced some interesting results [22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Non-Dual Multi-Granulation Neutrosophic Rough Set with Applications

Zhang

Shao

2019

Symmetry

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Multi-attribute decision-making (MADM) is a part of management decision-making and an important branch of the modern decision theory and method. MADM focuses on the decision problem of discrete and finite decision schemes. Uncertain MADM is an extension and development of classical multi-attribute decision making theory. When the attribute value of MADM is shown by neutrosophic number, that is, the attribute value is complex data and needs three values to express, it is called the MADM problem in which the attribute values are neutrosophic numbers. However, in practical MADM problems, to minimize errors in individual decision making, we need to consider the ideas of many people and synthesize their opinions. Therefore, it is of great significance to study the method of attribute information aggregation. In this paper, we proposed a new theory—non-dual multi-granulation neutrosophic rough set (MS)—to aggregate multiple attribute information and solve a multi-attribute group decision-making (MGDM) problem where the attribute values are neutrosophic numbers. First, we defined two kinds of non-dual MS models, intersection-type MS and union-type MS. Additionally, their properties are studied. Then the relationships between MS, non-dual MS, neutrosophic rough set (NRS) based on neutrosophic intersection (union) relationship, and NRS based on neutrosophic transitive closure relation of union relationship are outlined, and a figure is given to show them directly. Finally, the definition of non-dual MS on two universes is given and we use it to solve a MGDM problem with a neutrosophic number as the attribute value.

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“…Li et al investigated the relationship between multigranulation rough sets and concept lattices via rule acquisition Yang et al 2009). Kumar and Inbarani applied rough set based data mining techniques for medical data to discover locally frequent diseases (Senthil Kumar and Hannah Inbarani 2015). Huang et al (2014) developed a new multigranulation rough set model that was called intuitionistic fuzzy multigranulation rough set (IFMGRS) and three types of IFMGRSs that are generalizations of three existing intuitionistic fuzzy rough set models built.…”

mentioning

confidence: 99%

Generalized multigranulation rough sets and optimal granularity selection

Zhang

2017

Granul. Comput.

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Optimistic Multi-granulation Rough Set Based Classification for Medical Diagnosis

Cited by 45 publications

References 17 publications

RETRACTED ARTICLE: Covering-based rough set classification system

RETRACTED ARTICLE: Covering-based rough set classification system

Non-Dual Multi-Granulation Neutrosophic Rough Set with Applications

Generalized multigranulation rough sets and optimal granularity selection

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