Rough set based maximum relevance-maximum significance criterion and Gene selection from microarray data

Maji, Pradipta; Paul, Sushmita

doi:10.1016/j.ijar.2010.09.006

Cited by 125 publications

(59 citation statements)

References 49 publications

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“…In recent decades, rough sets and fuzzy-rough sets theories have been employed in various application areas such as data mining [4,5,86,[301][302][303], software packages [141,304], web ontology [138,[305][306][307], pattern recognition [24,148,187,[308][309][310], granular computing [38,221,238,251], genetic algorithm [310][311][312][313], prototype selection [145,163], solid transportation [146,314,315], social networks [316][317][318], artificial neural network [92,153,319], remote sensing [320,321], and gene selection [158,[322][323][324]]. An et al [140] analysed a regression algorithm based on fuzzy partition, fuzzy-rough sets, estimation of regression values, and fuzzy approximation for estimating wind speed.…”

Section: Distribution Of Papers Based On Other Application Areasmentioning

confidence: 99%

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

et al. 2017

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Rough set theory has been used extensively in fields of complexity, cognitive sciences, and artificial intelligence, especially in numerous fields such as expert systems, knowledge discovery, information system, inductive reasoning, intelligent systems, data mining, pattern recognition, decision-making, and machine learning. Rough sets models, which have been recently proposed, are developed applying the different fuzzy generalisations. Currently, there is not a systematic literature review and classification of these new generalisations about rough set models. Therefore, in this review study, the attempt is made to provide a comprehensive systematic review of methodologies and applications of recent generalisations discussed in the area of fuzzy-rough set theory. On this subject, the Web of Science database has been chosen to select the relevant papers. Accordingly, the systematic and metaanalysis approach, which is called "PRISMA," has been proposed and the selected articles were classified based on the author and year of publication, author nationalities, application field, type of study, study category, study contribution, and journal in which the articles have appeared. Based on the results of this review, we found that there are many challenging issues related to the different application area of fuzzy-rough set theory which can motivate future research studies.

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Section: Distribution Of Papers Based On Other Application Areasmentioning

confidence: 99%

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

et al. 2017

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“…It is a useful tool for dealing with vague, uncertain, and incomplete information. Based on classical RS models, the selection criteria are constructed using feature dependence and significance measure for feature selection (Maji and Paul, 2011). However, some RS models can only deal with data with nominal features, and thus the datasets must be discretized before feature selection.…”

Section: Improved Rs-based Feature Selectionmentioning

confidence: 99%

Locally linear embedding and neighborhood rough set-based gene selection for gene expression data classification

Sun

Wang

et al. 2016

Genet. Mol. Res.

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ABSTRACT. Cancer subtype recognition and feature selection are important problems in the diagnosis and treatment of tumors. Here, we propose a novel gene selection approach applied to gene expression data classification. First, two classical feature reduction methods including locally linear embedding (LLE) and rough set (RS) are summarized. The advantages and disadvantages of these algorithms were analyzed and an optimized model for tumor gene selection was developed based on LLE and neighborhood RS (NRS). Bhattacharyya distance was introduced to delete irrelevant genes, pair-wise redundant analysis was performed to remove strongly correlated genes, and the wavelet soft threshold was determined to eliminate noise in the gene datasets. Next, prior optimized search processing was carried out. A new approach combining dimension reduction of LLE and feature reduction of NRS (LLE-NRS) was developed for selecting gene subsets, and then an open source software Weka was applied to distinguish different tumor types and verify the cross-validation classification accuracy of our proposed method. The experimental results demonstrated that the classification performance of the proposed LLE-NRS for selecting gene subset outperforms those of other related models in terms of accuracy, and our proposed approach is feasible and effective in the field of highdimensional tumor classification.

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“…It has been a challenging problem to identify the genes that are relevant or not to a clinical diagnosis [1,5,15]. As feature selection methods, mutual information [6,7,13], the t-test [14], threshold number of misclassifications (TNoM) score [8], and the Bhattacharyya distance [3,4,21] have been widely used in finding relevant genes. Feature selection methods have been used for pattern recognition and machine learning [10].…”

Section: Introductionmentioning

confidence: 99%

“…As specific classifiers in machine learning, k-nearest neighbor (k-NN) [8], support vector machine (SVM) [8,11,12], and rough set [13] have all been used to verify the efficiency after selecting the genes.…”

Section: Introductionmentioning

confidence: 99%

A hierarchical two-phase framework for selecting genes in cancer datasets with a neuro-fuzzy system

Lim¹,

Wang²,

Lim

2016

THC

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Abstract. Finding the minimum number of appropriate biomarkers for specific targets such as a lung cancer has been a challenging issue in bioinformatics. We propose a hierarchical two-phase framework for selecting appropriate biomarkers that extracts candidate biomarkers from the cancer microarray datasets and then selects the minimum number of appropriate biomarkers from the extracted candidate biomarkers datasets with a specific neuro-fuzzy algorithm, which is called a neural network with weighted fuzzy membership function (NEWFM). In this context, as the first phase, the proposed framework is to extract candidate biomarkers by using a Bhattacharyya distance method that measures the similarity of two discrete probability distributions. Finally, the proposed framework is able to reduce the cost of finding biomarkers by not receiving medical supplements and improve the accuracy of the biomarkers in specific cancer target datasets.

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Rough set based maximum relevance-maximum significance criterion and Gene selection from microarray data

Cited by 125 publications

References 49 publications

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

Locally linear embedding and neighborhood rough set-based gene selection for gene expression data classification

A hierarchical two-phase framework for selecting genes in cancer datasets with a neuro-fuzzy system

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