Feature Selection Using Fuzzy Neighborhood Entropy-Based Uncertainty Measures for Fuzzy Neighborhood Multigranulation Rough Sets

Sun, Lin; Wang, Lanying; Ding, Weiping; Qian, Yuhua; Xu, Jiucheng

doi:10.1109/tfuzz.2020.2989098

Cited by 183 publications

(60 citation statements)

References 45 publications

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“…In this subsection, to verify the classification effectiveness of the FNSIJE-KS method on UCI datasets, we compare the size and classification accuracy of selected feature subsets by FNSIJE-KS and three existing methods. The three methods include FNRS (method based on fuzzy neighborhood rough set) [32], FNCE (method based on fuzzy neighborhood conditional entropy) [37], and FNPME-FS (method based on fuzzy neighborhood pessimistic multigranulation entropy) [36].…”

Section: Classification Results Of the Uci Datasetsmentioning

confidence: 99%

“…The second portion pays attention to the effectiveness of classification for FNSIJE-KS. Tables 8 and 9 list classification accuracy of selected feature subsets by FNSIJE-KS and other three feature selection methods (FNRS [32], FNCE [37], and FNPME-FS [36]) under classifiers KNN and CART, respectively. In Tables 8 and 9, the bolded numbers indicate that the classification accuracy of the selected feature subsets is the best with respect to other methods.…”

Section: Classification Results Of the Uci Datasetsmentioning

confidence: 99%

“…Yue et al [35] developed fuzzy neighborhoods to be applied to cover data classification. Sun et al [36] proposed a new fuzzy neighborhood multi-granularity rough set model by combining FNRS with multi-granularity rough set model, which expanded the type of rough set model. Xu et al [37] redefined the fuzzy neighborhood relationship in FNRS and introduced it into conditional entropy, and proposed a new model-fuzzy neighborhood conditional entropywhich improved the measurement mechanism.…”

Section: Related Workmentioning

confidence: 99%

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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: Classification Results Of the Uci Datasetsmentioning

confidence: 99%

Section: Classification Results Of the Uci Datasetsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Yuan

2021

Complex Intell. Syst.

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“…It is intended to find the concealed linear structures in the original data [ 4 , 5 ]. For the sake of transition from linear to nonlinear function, the following generalization can be made [ 6 ]: by mapping input vectors into a high-dimensional feature space H ( H is Hilbert space) through some nonlinear-mapping, seek the solution of the optimization problem in space H . Using a suitable kernel function

, nonlinear-mappings can be estimated by kernel

, which is an extended

with kernel techniques.…”

Section: Introductionmentioning

confidence: 99%

LSSVR Model of G-L Mixed Noise-Characteristic with Its Applications

Zhang

Zhou

Sun

et al. 2020

Entropy

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Due to the complexity of wind speed, it has been reported that mixed-noise models, constituted by multiple noise distributions, perform better than single-noise models. However, most existing regression models suppose that the noise distribution is single. Therefore, we study the Least square S V R of the Gaussian–Laplacian mixed homoscedastic ( G L M − L S S V R ) and heteroscedastic noise ( G L M H − L S S V R ) for complicated or unknown noise distributions. The ALM technique is used to solve model G L M − L S S V R . G L M − L S S V R is used to predict short-term wind speed with historical data. The prediction results indicate that the presented model is superior to the single-noise model, and has fine performance.

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“…However, there exists the problem of information loss during the conversion. The latter algorithm adaptation extends the specific learning methods to deal with multilabel data directly [13], including neural works [14]- [16], mutual information [17]- [19], ReliefF [20], [21], etc., where deep learning based on neural network models [22]- [24] performs representation learning on data and has achieved many results in image processing and natural language processing [25], [26]. Unfortunately, their time-space cost is expensive.…”

Section: Introductionmentioning

confidence: 99%

Multilabel Feature Selection Using Mutual Information and ML-ReliefF for Multilabel Classification

Shi

Sun

et al. 2020

IEEE Access

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Recently, multilabel classification algorithms play an increasingly significant role in data mining and machine learning. However, some existing mutual information-based algorithms ignore the influence of the proportions of labels on the correlation degree between features and label sets. Besides, the correlation degree of label sets cannot be accurately measured in most traditional ReliefF algorithms, and the repeated calculation arises from the division of heterogeneous neighbors. To overcome these shortcomings, this paper proposes a multilabel feature selection method using mutual information and improved multilabel ReliefF (ML-ReliefF). First, the proportion of each label is calculated in label space and combined with the mutual information of features and labels to construct a novel correlation degree between features and label sets to preprocess multilabel datasets, which is used to reduce runtime of ML-ReliefF. Second, the mutual information of label sets is introduced into improving accuracy of the correlation degree among label sets. Furthermore, two types of correlation degree for label sets based on ML-ReliefF are developed to divide similar and heterogeneous samples more clearly. Third, a divided method of heterogeneous neighbors is presented to effectively avoid the repeated calculation in ML-ReliefF, and a novel method of feature weighting based on ML-ReliefF is constructed to evaluate the importance of features. Finally, a multilabel feature selection algorithm based on mutual information and ML-ReliefF for multilabel classification is designed to improve the performance of multilabel classification. Experiments under fourteen multilabel datasets show the effectiveness of our algorithm and improve the classification performance for multilabel datasets.

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Feature Selection Using Fuzzy Neighborhood Entropy-Based Uncertainty Measures for Fuzzy Neighborhood Multigranulation Rough Sets

Cited by 183 publications

References 45 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

LSSVR Model of G-L Mixed Noise-Characteristic with Its Applications

Multilabel Feature Selection Using Mutual Information and ML-ReliefF for Multilabel Classification

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