A Fitting Model for Feature Selection With Fuzzy Rough Sets

Wang, Changzhong; Qi, Yongsheng; Shao, Mingwen; Hu, Qinghua; Chen, Degang; Qian, Yuhua; Lin, Yaojin

doi:10.1109/tfuzz.2016.2574918

Cited by 208 publications

(38 citation statements)

References 58 publications

(58 reference statements)

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“…By getting inspiration from these studies, we hybridize rough sets with soft sets. Hence, the given data will be represented in Boolean-valued information systems through soft sets on which formal approximation can be applied for data reduction using rough sets [35,40,41].…”

Section: Soft-rough Sets For Handling Contextual Sparsitymentioning

confidence: 99%

A Soft-Rough Set Based Approach for Handling Contextual Sparsity in Context-Aware Video Recommender Systems

2019

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Context-aware video recommender systems (CAVRS) seek to improve recommendation performance by incorporating contextual features along with the conventional user-item ratings used by video recommender systems. In addition, the selection of influential and relevant contexts has a significant effect on the performance of CAVRS. However, it is not guaranteed that, under the same contextual scenario, all the items are evaluated by users for providing dense contextual ratings. This problem cause contextual sparsity in CAVRS because the influence of each contextual factor in traditional CAVRS assumes the weights of contexts homogeneously for each of the recommendations. Hence, the selection of influencing contexts with minimal conflicts is identified as a potential research challenge. This study aims at resolving the contextual sparsity problem to leverage user interactions at varying contexts with an item in CAVRS. This problem may be investigated by considering a formal approximation of contextual attributes. For the purpose of improving the accuracy of recommendation process, we have proposed a novel contextual information selection process using Soft-Rough Sets. The proposed model will select a minimal set of influencing contexts using a weights assign process by Soft-Rough sets. Moreover, the proposed algorithm has been extensively evaluated using “LDOS-CoMoDa” dataset, and the outcome signifies the accuracy of our approach in handling contextual sparsity by exploiting relevant contextual factors. The proposed model outperforms existing solutions by identifying relevant contexts efficiently based on certainty, strength, and relevancy for effective recommendations.

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Section: Soft-rough Sets For Handling Contextual Sparsitymentioning

confidence: 99%

A Soft-Rough Set Based Approach for Handling Contextual Sparsity in Context-Aware Video Recommender Systems

2019

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“…Wang [114] defined an upper approximation number for developing a quantitative analysis of covering-based rough set theory. Wang et al [25] proposed a fitting fuzzy-rough set model to conduct feature selection. Yang and Hu [178] proposed a definition of fuzzy -covering approximation spaces by introducing some new definitions of fuzzy -covering approximation spaces, Ma's fuzzy covering-based rough set and the properties of fuzzy -covering approximation spaces.…”

Section: Related Workmentioning

confidence: 99%

“…Rough set theory, introduced by Pawlak [3] in the 1980s, is a powerful machine learning tool that has applications in many data mining [4][5][6][7][8][9][10][11] instances, attribute and feature selection [12][13][14][15][16][17][18][19][20][21][22][23][24][25], and data prediction [26,27]. Rough set theory deals with information systems that contain inconsistent data, such as two patients who have the same symptoms but different diseases.…”

Section: Introductionmentioning

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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“…Rough set theory [1] presented by Pawlak in 1982 is a useful tool to deal with vagueness and uncertainty information in the field of computer sciences. The research of rough set theory has mainly focused on both the generalizations of rough set models and the applications in different data environments, which has already attached much attention in granular computing [2][3][4], feature selection [5][6][7][8], dynamic data mining [9][10][11], and big data mining [12,13]. On the other hand, since the information entropy is powerful to measure information uncertainty, it has been extensively applied in practical problems, such as decision making [14], time series [15], portfolio selection [16], and so on.…”

Section: Introductionmentioning

confidence: 99%

A Fast Feature Selection Algorithm by Accelerating Computation of Fuzzy Rough Set-Based Information Entropy

Xiao

Liu

Yang

2018

Entropy

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The information entropy developed by Shannon is an effective measure of uncertainty in data, and the rough set theory is a useful tool of computer applications to deal with vagueness and uncertainty data circumstances. At present, the information entropy has been extensively applied in the rough set theory, and different information entropy models have also been proposed in rough sets. In this paper, based on the existing feature selection method by using a fuzzy rough set-based information entropy, a corresponding fast algorithm is provided to achieve efficient implementation, in which the fuzzy rough set-based information entropy taking as the evaluation measure for selecting features is computed by an improved mechanism with lower complexity. The essence of the acceleration algorithm is to use iterative reduced instances to compute the lambda-conditional entropy. Numerical experiments are further conducted to show the performance of the proposed fast algorithm, and the results demonstrate that the algorithm acquires the same feature subset to its original counterpart, but with significantly less time.

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A Fitting Model for Feature Selection With Fuzzy Rough Sets

Cited by 208 publications

References 58 publications

A Soft-Rough Set Based Approach for Handling Contextual Sparsity in Context-Aware Video Recommender Systems

A Soft-Rough Set Based Approach for Handling Contextual Sparsity in Context-Aware Video Recommender Systems

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

A Fast Feature Selection Algorithm by Accelerating Computation of Fuzzy Rough Set-Based Information Entropy

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