Feature Selection for Multi-Label Learning Based on F-Neighborhood Rough Sets

Deng, Zhixuan; Zheng, Zhonglong; Deng, Dayong; Wang, Tianxiang; He, Yiran; Zhang, Dawei

doi:10.1109/access.2020.2976162

Cited by 12 publications

(5 citation statements)

References 46 publications

(65 reference statements)

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“…Pawlak proposed the classic rough set model in 1982 [18], which was successfully applied in the field of feature selection [19][20][21][22][23][24][25][26][27]. However, the Pawlak proposed that rough set is based on the general binary relationship and is only suitable for discrete data [28].…”

Section: Related Workmentioning

confidence: 99%

Feature selection using self-information and entropy-based uncertainty measure for fuzzy neighborhood rough set

Yuan

2021

Complex Intell. Syst.

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Feature selection based on the fuzzy neighborhood rough set model (FNRS) is highly popular in data mining. However, the dependent function of FNRS only considers the information present in the lower approximation of the decision while ignoring the information present in the upper approximation of the decision. This construction method may lead to the loss of some information. To solve this problem, this paper proposes a fuzzy neighborhood joint entropy model based on fuzzy neighborhood self-information measure (FNSIJE) and applies it to feature selection. First, to construct four uncertain fuzzy neighborhood self-information measures of decision variables, the concept of self-information is introduced into the upper and lower approximations of FNRS from the algebra view. The relationships between these measures and their properties are discussed in detail. It is found that the fourth measure, named tolerance fuzzy neighborhood self-information, has better classification performance. Second, an uncertainty measure based on the fuzzy neighborhood joint entropy has been proposed from the information view. Inspired by both algebra and information views, the FNSIJE is proposed. Third, the K–S test is used to delete features with weak distinguishing performance, which reduces the dimensionality of high-dimensional gene datasets, thereby reducing the complexity of high-dimensional gene datasets, and then, a forward feature selection algorithm is provided. Experimental results show that compared with related methods, the presented model can select less important features and have a higher classification accuracy.

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Section: Related Workmentioning

confidence: 99%

Feature selection using self-information and entropy-based uncertainty measure for fuzzy neighborhood rough set

Yuan

2021

Complex Intell. Syst.

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“…Attribute or feature selection is the process of selecting a subset of relevant attributes or variables from all features by eliminating the redundant and irrelevant attributes in a dataset [19]. The selected subset of relevant attributes will be used in predictive model building.…”

Section: Literature Reviewmentioning

confidence: 99%

Individual Attribute Selection Using Information Gain Based Distance for Group Classification of Elderly People With Hypertension

2021

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Attribute selection is the process of selecting relevant attributes being used in model construction to enhance model accuracy. For general medical oriented classification applications, classical attribute selection methods principally select common attributes in the dataset for all individuals. The idea of using individual attributes is proposed in this study to represent the difference among individuals for self-diagnosis. Consequently, this study proposes a new attribute selection method, called information gain based distance (IGD), for individual attribute selection, which represents an individual's health condition differently and can be used for effective classification. The proposed method combines the concept of information gain and objective distance to select individual attributes affecting classification. The IGD method is expected to provide higher classification performance than classical attribute selection methods. To assess the performance of the IGD method, classification accuracy between data with classical attribute selections and with the IGD method is compared. The case study is conducted with 971 secondary data used for group classification of elderly people with hypertension. The classification result of different classifiers was compared, including K-nearest neighbors, neural network, and naive Bayes. The comparison revealed that the classification of data with the IGD attribute selection method provided an average classification accuracy of 98.73%. In comparison, those classifications of data with classical attribute selection methods provided 62.99%, 62.99%, 62.62%, and 62.85% for information gain, Gini index, chisquared, and decision tree, respectively. The results showed that data classification with the IGD method provided higher performance than those with the classical attribute selection methods.

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“…Emerging as an innovation in classical rough set theory, neighborhood rough set (NRS) theory was put forward by Lin in 1988 [19,20]. e idea of NRS algorithm is that, in the real space, each data point will form a neighborhood δ B (x i ) and the data in the neighborhood family will constitute the basic information particles [21][22][23].…”

Section: Reduction Feature Factorsmentioning

confidence: 99%

Prediction of the RFID Identification Rate Based on the Neighborhood Rough Set and Random Forest for Robot Application Scenarios

Wang

Pan

et al. 2020

Complexity

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With the rapid development of Internet of Things technology, RFID technology has been widely used in various fields. In order to optimize the RFID system hardware deployment strategy and improve the deployment efficiency, the prediction of the RFID system identification rate has become a new challenge. In this paper, a neighborhood rough set and random forest (NRS-RF) combination model is proposed to predict the identification rate of an RFID system. Firstly, the initial influencing factors of the RFID system identification rate are reduced using neighborhood rough set theory combined with the principle of heuristic attribute reduction of neighborhood weighted dependency, thus obtaining a kernel factor subset. Secondly, a random forest prediction model is established based on the kernel factor subset, and a confusion matrix is established using out-of-bag (OOB) data to evaluate the prediction results. The test is conducted under the constructed RFID experimental environment, whose results showed that the model can predict the identification rate of the RFID system in a fast and efficient way, and the classification accuracy can reach 90.5%. It can effectively guide the hardware deployment and communication parameter protocol setting of the system and improve the system performance. Compared with BP neural network (BPNN) and other prediction models, NRS-RF has shorter prediction time and faster calculation speed. Finally, the validity of the proposed model was verified by the RFID intelligent archives management platform.

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Feature Selection for Multi-Label Learning Based on F-Neighborhood Rough Sets

Cited by 12 publications

References 46 publications

Feature selection using self-information and entropy-based uncertainty measure for fuzzy neighborhood rough set

Feature selection using self-information and entropy-based uncertainty measure for fuzzy neighborhood rough set

Individual Attribute Selection Using Information Gain Based Distance for Group Classification of Elderly People With Hypertension

Prediction of the RFID Identification Rate Based on the Neighborhood Rough Set and Random Forest for Robot Application Scenarios

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