A novel attribute reduction approach for multi-label data based on rough set theory

Li, Hua; Li, Deyu; Zhai, Yongjie; Wang, Suge; Zhang, Jing

doi:10.1016/j.ins.2016.07.008

Cited by 77 publications

(26 citation statements)

References 33 publications

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“…is a set of decision attributes. In general, T ∩ C = ∅ and each attribute from T ∩ C forms a mapping f : U → V , where V is the value domain of T and C [27,28]. Each nonempty subset P ⊆ T determines an indiscernibility relation as…”

Section: The Rough Set Theorymentioning

confidence: 99%

Green-Building-Material Supplier Selection with a Rough-Set-Enhanced Quality Function Deployment

et al. 2019

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Building material supplier evaluation and selection is a significant strategic-decision problem for reducing construction costs and ensuring the quality of a residential product. As people are increasingly concerning about the green level of a residential product and the competition in the housing market is becoming increasingly fierce, it becomes important to select a green customer-oriented material supplier for property developers. Quality function deployment (QFD) has been proven to be an effective quality-control technique to take customer voices into consideration. However, the relationship matrix in the QFD technique, as a key to translate customer requirements into technical attributes, was subjectively given by decision-makers in previous studies, which failed to reflect customer requirements accurately. The aim of this study is to put forward a neighborhood-rough-set-based quality function deployment model for a green-building-material supplier selection. The neighborhood rough set, as a nonparametric and flexible data-mining approach, can effectively and objectively determine the core relationships between a variety of factors. A rough number-based aggregation approach is applied to effectively and objectively aggregate the evaluations given by a group of experts. Then, the classical double normalization-based multiple aggregation method, which considers two types of normalization methods, three aggregation models, and a comprehensive score formula, is extended in rough-number form in order to rank the alternatives. Afterward, an attempt is made to evaluate and rank eleven alternative building-material suppliers for a repute property developer in mainland China, and the corresponding comparative and sensitive analyses verify the effectiveness and robustness of the proposed hybrid model.

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Section: The Rough Set Theorymentioning

confidence: 99%

Green-Building-Material Supplier Selection with a Rough-Set-Enhanced Quality Function Deployment

et al. 2019

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“…For example, the RS theory mainly focuses on the attribute reduction problem [23,28] and shows much potential in the multilabel learning [29,36], while the PCA is usually applied to the dimension reduction problem in the machine learning [9,16] and multi-objective optimization (MOO) [18,20]. What's more, the RS theory reduces attributes based on the partition of equivalence relation, which can be accomplished through the evaluation metric [30,31], like mutual information and information entropy [10,38]. In general, the RS theory achieves better performance on the categorical attribute dataset [1,11].…”

Section: Literature Review 21 Index System Reduction Algorithmsmentioning

confidence: 99%

Index System Reduction Method Based on the Index Similarity

Gao

2019

Teh. vjesn.

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Multi-attribute decision making (MADM) always suffers from the result inconsistency and computational complexity problem, due to numbers of redundant and relational attributes (indexes) of the initial evaluation index system. Therefore, this paper studies the index system (IS) reduction problem through selecting the most representative indicator from each index subsystem after the IS structure partition. First, we propose and demonstrate the Index Subsystem Judgement theorem to improve the efficiency of the classic system structure partition algorithm. Second, an algorithm of index system reduction based on the index similarity (ISRS) is put forward. The ISRS is able to reduce the index quantity while still keeping the index meaning. Third, we define the direction loss rate to measure the evaluation ability loss of the IS during reduction. The algorithm is tested for a synthetic dataset to compare the proposed ISRS with different index reduction algorithms, followed by an extensive experimentation with a real-world financial dataset. Experiment results illustrate that our proposed method is able to obtain more accessible and available reduction results in practice.

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“…Another disadvantage of this classifier is that it does not take into consideration the dependency between labels. However, it is still one of the most popular ML classifiers that is frequently used in many ML-FS papers (Doquire & Verleysen, 2013a;Jungjit & Freitas, 2015a;Lee & Kim, 2015a;Li, Li, Zhai, Wang, & Zhang, 2016;Lin, Hu, Liu, Chen, & Duan, 2016).…”

Section: Knn-based ML Classifiersmentioning

confidence: 99%

Multilabel feature selection: A comprehensive review and guiding experiments

Kashef

Nezamabadi‐pour

Nikpour

2018

WIREs Data Min & Knowl

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Feature selection has been an important issue in machine learning and data mining, and is unavoidable when confronting with high‐dimensional data. With the advent of multilabel (ML) datasets and their vast applications, feature selection methods have been developed for dimensionality reduction and improvement of the classification performance. In this work, we provide a comprehensive review of the existing multilabel feature selection (ML‐FS) methods, and categorize these methods based on different perspectives. As feature selection and data classification are closely related to each other, we provide a review on ML learning algorithms as well. Also, to facilitate research in this field, a section is provided for setup and benchmarking that presents evaluation measures, standard datasets, and existing software for ML data. At the end of this survey, we discuss some challenges and open problems in this field that can be pursued by researchers in future. WIREs Data Mining Knowl Discov 2018, 8:e1240. doi: 10.1002/widm.1240 This article is categorized under: Technologies > Data Preprocessing

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A novel attribute reduction approach for multi-label data based on rough set theory

Cited by 77 publications

References 33 publications

Green-Building-Material Supplier Selection with a Rough-Set-Enhanced Quality Function Deployment

Green-Building-Material Supplier Selection with a Rough-Set-Enhanced Quality Function Deployment

Index System Reduction Method Based on the Index Similarity

Multilabel feature selection: A comprehensive review and guiding experiments

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