Mining from incomplete quantitative data by fuzzy rough sets

Hong, Tzung-Pei; Tseng, Li-Huei; Chien, Been-Chian

doi:10.1016/j.eswa.2009.08.002

Cited by 36 publications

(12 citation statements)

References 19 publications

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“…As shown in Table 1, the sparse degree 0.25 τ = , because the information table is non-sparse, both SIM-EM algorithm and Prediction-EM use formula (2) to estimate, therefore we get the same accuracy rating C . As shown in Table 2, the sparse degree 0.54 τ = , the information table is sparse, SIM-EM algorithm and Prediction-EM algorithm is respectively used to estimate, the result shown as Table 3.…”

Section: A Instance Analysismentioning

confidence: 94%

“…However, in reality, the information system is usually an incomplete information system, thus, there is some uncertain information in the system which is shown as a non-value data table (it contains one or more non-value). Four uncertain factors are analyzed in literature [2]: discrete treatment, non-precise data, missing values, multiple descriptors. Missing value is null value which indicates the corresponding attribute value unknown or unavailable.…”

Section: Introductionmentioning

confidence: 99%

“…Prediction-EM Algorithm (Null values estimation Based on similarity predictions in Rough Sets) Calculate sparse degree τ of informaiton table;3) If 0 τ < ≤ k ，then according to formula(2) to estimate the unknown attribute value…”

mentioning

confidence: 99%

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A Null Value Estimation Method Based on Similarity Predictions in Rough Sets

Yang

Zhang

et al. 2010

2010 Fifth International Conference on Internet Computing for Science and Engineering

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In this paper, we utilize rough set theory as a tool to deal with the problem of null-value estimation in an incomplete information system, a rating mechanism in collaborative filtering technology is introduced into this paper for the weakness of null value estimation based on similar relational algorithm (SIM-EM), such as no sparse degree process and low accuracy, and an improved null value estimation method, which based on SIM-EM is proposed from the perspective of similarity. The null value data is predicted and filled through the similarity of objects; otherwise a dual feature weight method is proposed according to the attribute's feature in rough set, which improves the accuracy in similarity calculation; the improved algorithm is good at dealing with sparse rough set, and the accuracy and the mean absolute error is better than the original method.

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Section: A Instance Analysismentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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A Null Value Estimation Method Based on Similarity Predictions in Rough Sets

Yang

Zhang

et al. 2010

2010 Fifth International Conference on Internet Computing for Science and Engineering

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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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“…The paper [2] gave a new approach for mining incomplete data with singleton, subset and concept probabilistic approximations. The paper [3] proposed a method for mining incomplete quantitative data sets by rough sets. The paper [4] proposed a new approach of mining missing values for optimizing semiconductor-manufacturing processes.…”

Section: Introductionmentioning

confidence: 99%