Fuzzy–Rough Sets for Information Measures and Selection of Relevant Genes From Microarray Data

Maji, Pradipta; Pal, Sankar K.

doi:10.1109/tsmcb.2009.2028433

Cited by 83 publications

(44 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…Zhao et al [157] developed a rulebased classifier fuzzy-rough using one generalised fuzzyrough set model to introduce a novel idea which was called consistence degree. Maji and Pal [158] presented a new fuzzy equivalence partition matrix for approximating the true marginal and joint distributions of continuous gene expression values. Huang and Kuo [159] investigated two perspectives of cross-lingual semantic document similarity measures based on the fuzzy sets and rough sets which were named formulation of similarity measures and document representation.…”

Section: Distribution Of Papers Based On Other Application Areasmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2017

View full text Add to dashboard Cite

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.

show abstract

Section: Distribution Of Papers Based On Other Application Areasmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…Pradipta Maji et al [23] has discussed that the use of many information measures like entropy, mutual information, and finformation has been proved to be successful for choosing a set of relevant and non redundant genes from a high-dimensional micro array data set. But determining the true density functions and carrying out the integrations necessary to calculate diverse information measures is extremely difficult for continuous gene expression values Consequently, the true marginal and joint distributions of continuous gene expression values have been approximated by introducing the concept of the fuzzy equivalence partition matrix.…”

Section: Related Workmentioning

confidence: 99%

Classification of Micro Array Gene Expression Data using Statistical Analysis Approach with Personalized Fuzzy Inference System

Madeswaran¹,

Nawaz²

2011

IJCA

View full text Add to dashboard Cite

In this paper we describe a method to classify the micro array gene expression data based on their tissue sample types. Normally dimensionality reduction is performed over the micro array gene expression data set. Here, we propose a statistical approach for extracting significant genes from the gene expression data set. But, the statistical approach does not correctly identify the important genes. Hence, the ultimate objective is to solve the drawbacks in dimensionality reduction as they have a direct impact on the robustness of the generated fuzzy rules. Consequently, the goal is to generate fuzzy rules based on dimensionality reduced data. Hence, fuzzy inference is selected in our approach for classification and the fuzzy rules are utilized to train the fuzzy inference system (FIS). The classification performance of the fuzzy inference system (FIS) is similar to that of other classifiers, but simpler and easier to interpret. The classification performance of the FIS classifier is compared over the existing Fuzzy Genetic, Fuzzy Neural Network ProbPCA and PCA classifiers. The classification performance of the proposed technique is evaluated over the cancer datasets of Acute myeloid leukemia (AML) and Acute Lymphoblastic Leukemia (ALL).

show abstract

“…When we perform attribute reduction using RST, the main goal is to find the minimum attribute set and induce minimal length decision rules inherent in the information system with affordable algorithmic complexity and computational cost [4][5] [6]. So, it usually refers to the preferred technique for data preprocessing in data mining and knowledge discovery [7]- [11].…”

Section: Introductionmentioning

confidence: 99%

A Layered-Coevolution-Based Attribute-Boosted Reduction Using Adaptive Quantum-Behavior PSO and Its Consistent Segmentation for Neonates Brain Tissue

Ding

Lin

Prasad

et al. 2018

IEEE Trans. Fuzzy Syst.

View full text Add to dashboard Cite

Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.1063-6706 (c) 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TFUZZ.2017.2717381, IEEE Transactions on Fuzzy Systems 1 Abstract-The main challenge of attribute reduction in large data applications is to develop a new algorithm to deal with large, noisy, and uncertain large data linking multiple relevant data sources, structured or unstructured. This paper proposes a new and efficient layered-coevolution-based attribute-boosted reduction algorithm (LCQ-ABR*) using adaptive quantum behavior particle swarm optimization (PSO). First, the quantum rotation angle of an evolutionary particle is updated by a dynamic change of self-adapting step size. Second, a self-adaptive partitioning strategy is employed to group particles into different memeplexes, and the quantum-behavior mechanism with the particles' states depicted by the wave function cooperates to achieve superior performance in their respective memeplexes. Third, a new layered co-evolutionary model with multi-agent interaction is constructed to decompose a complex attribute set, and it can self-adapt the attribute sizes among different layers and produce the reasonable decompositions by exploiting any interdependency among multiple relevant attribute subsets. Fourth, the decomposed attribute subsets are evolved to compute the positive region and discernibility matrix by using their best quantum particles, and the global optimal reduction set is induced successfully. Finally, extensive comparative experiments are provided to illustrate that LCQ-ABR* has better feasibility and effectiveness of attribute reduction on large-scale and uncertain dataset problems with complex noise, compared with Index Terms-Attribute-boosted reduction, adaptive quantum behavior PSO, layered-coevolution with multi-agent interaction, consistent segmentation for neonates brain tissue, sulci and gyrus estimate.

show abstract

Fuzzy–Rough Sets for Information Measures and Selection of Relevant Genes From Microarray Data

Cited by 83 publications

References 17 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

Classification of Micro Array Gene Expression Data using Statistical Analysis Approach with Personalized Fuzzy Inference System

A Layered-Coevolution-Based Attribute-Boosted Reduction Using Adaptive Quantum-Behavior PSO and Its Consistent Segmentation for Neonates Brain Tissue

Contact Info

Product

Resources

About