Multi-label feature selection based on fuzzy neighborhood rough sets

Xu, Jiucheng; Shen, Kaili; Sun, Lin

doi:10.1007/s40747-021-00636-y

Cited by 35 publications

(9 citation statements)

References 64 publications

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“…With the rapid development of information technology, the databases expand rapidly. In daily production and life, more and more information is obtained and stored [1][2][3][4][5]. However, these information may contain a great quantity of redundancy, noise, or even missing feature values [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Feature selection based on self-information and entropy measures for incomplete neighborhood decision systems

Yuan

et al. 2022

Complex Intell. Syst.

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For incomplete datasets with mixed numerical and symbolic features, feature selection based on neighborhood multi-granulation rough sets (NMRS) is developing rapidly. However, its evaluation function only considers the information contained in the lower approximation of the neighborhood decision, which easily leads to the loss of some information. To solve this problem, we construct a novel NMRS-based uncertain measure for feature selection, named neighborhood multi-granulation self-information-based pessimistic neighborhood multi-granulation tolerance joint entropy (PTSIJE), which can be used to incomplete neighborhood decision systems. First, from the algebra view, four kinds of neighborhood multi-granulation self-information measures of decision variables are proposed by using the upper and lower approximations of NMRS. We discuss the related properties, and find the fourth measure-lenient neighborhood multi-granulation self-information measure (NMSI) has better classification performance. Then, inspired by the algebra and information views simultaneously, a feature selection method based on PTSIJE is proposed. Finally, the Fisher score method is used to delete uncorrelated features to reduce the computational complexity for high-dimensional gene datasets, and a heuristic feature selection algorithm is raised to improve classification performance for mixed and incomplete datasets. Experimental results on 11 datasets show that our method selects fewer features and has higher classification accuracy than related methods.

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Section: Introductionmentioning

confidence: 99%

Feature selection based on self-information and entropy measures for incomplete neighborhood decision systems

Yuan

et al. 2022

Complex Intell. Syst.

Self Cite

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“…The Binary Relevance approach is compatible with the usual multi-label FS approach, which converts multi-label datasets into single-label datasets before applying classic FS algorithms [5]. The major issue in this technique is that the interdependency between the labels is frequently overlooked which in turn causes difficulty to investigate the structure of labels that improves the performance of the multi-label learning by reducing the dimensionality [6], [7]. The authors of [6] used the concept of fuzzy neighborhood rough sets to handle multi-label datasets.…”

Section: Introductionmentioning

confidence: 99%

“…The major issue in this technique is that the interdependency between the labels is frequently overlooked which in turn causes difficulty to investigate the structure of labels that improves the performance of the multi-label learning by reducing the dimensionality [6], [7]. The authors of [6] used the concept of fuzzy neighborhood rough sets to handle multi-label datasets. The authors of [7] carried out reduction of attributes for multi-label learning algorithms using fuzzy rough sets.…”

Section: Introductionmentioning

confidence: 99%

Multi Label Feature Selection Through Dual Hesitant q-Rung Orthopair Fuzzy Dombi Aggregation Operators

et al. 2022

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In this article, the feature selection (FS) process is taken as a multi criteria decision making (MCDM) problem. Also, to consider the impreciseness arising in the real time data, the values of the decision matrix procured after the ridge regression is fuzzified into dual hesitant q-rung orthopair fuzzy set. For the information fusion process, we have proposed various aggregation operators such as the Dual Hesitant q-rung orthopair fuzzy weighted Dombi arithmetic aggregation operator, Dual Hesitant q-rung orthopair fuzzy weighted Dombi geometric aggregation operator, Dual Hesitant q-rung orthopair fuzzy ordered weighted Dombi arithmetic aggregation operator and Dual Hesitant q-rung orthopair fuzzy ordered weighted Dombi geometric aggregation operator. A multi-label feature selection method is proposed using these MCDM techniques formed by the aggregation operators. This algorithm, initially, obtains the values of the decision matrix through the process of ridge regression. The weight vector required for the MCDM process is calculated using entropy. Further, the data are fuzzified and the MCDM process proposed using the aforementioned aggregation operators are utilized. A rank vector is obtained by utilizing the score function to select the desired number of features. It should be noted that through changing the aggregation operator, the algorithm can be altered. Experimental evaluation that compares the proposed method to other existing methods in terms of evaluation metrics demonstrates the effectiveness of the proposed method and their significance is also evaluated.

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“…For example, Duan et al 37 redefined the low and upper approximations of NRS in multi‐label learning, and proposed an MFS algorithm based on the proposed multi‐label NRS model. Xu et al 38 proposed a fuzzy NRS model for MFS. Unfortunately, the above methods suffered from neighborhood granularity sensitivity.…”

Section: Introductionmentioning

confidence: 99%

Neighborhood rough set based multi‐label feature selection with label correlation

Liu

et al. 2022

Concurrency and Computation

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Summary Neighborhood rough set (NRS) is considered as an effective tool for feature selection and has been widely used in processing high‐dimensional data. However, most of the existing methods are difficult to deal with multi‐label data and are lack of considering label correlation (LC), which is an important issue in multi‐label learning. Therefore, in this article, we introduce a new NRS model with considering LC. First, we explore LC by calculating the similarity relation between labels and divide the related labels into several label subsets. Then, a new neighborhood relation is proposed, which can solve the problem of neighborhood granularity selection by using the nearest neighbor information distribution of instances under the related labels. On this basis, the NRS model is reconstructed by embedding LC information, and the related properties of the model are discussed. Moreover, we design a new feature significance function to evaluate the quality of features, which can well capture the specific relationship between features and labels. Finally, a greedy forward feature selection algorithm is designed. Extensive experiments which are conducted on different types of datasets verify the effectiveness of the proposed algorithm.

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Multi-label feature selection based on fuzzy neighborhood rough sets

Cited by 35 publications

References 64 publications

Feature selection based on self-information and entropy measures for incomplete neighborhood decision systems

Feature selection based on self-information and entropy measures for incomplete neighborhood decision systems

Multi Label Feature Selection Through Dual Hesitant q-Rung Orthopair Fuzzy Dombi Aggregation Operators

Neighborhood rough set based multi‐label feature selection with label correlation

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