Multi-Granulation Entropy and Its Applications

Zeng, Kai; She, Kun; Niu, Xinjian

doi:10.3390/e15062288

Cited by 18 publications

(7 citation statements)

References 20 publications

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“…Zhang et al [17] proposed information entropy based on fuzzy rough set and applied it in fuzzy information system. The importance of features based on the information view only explains the impact of uncertainty classification on features [17,37,44,45]. If combining the two views for feature selection, it helps to improve the quality of uncertainty measurement in decision system.…”

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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“…The data sets are downloaded from the UCI machine learning repository (http://archive.ics.uci. edu/ml/index.php) as [3] and are described in Table 3. The numerical attributes of the samples are linearly normalized as follows:…”

Section: Experimental Analysismentioning

confidence: 99%

“…This theory has been successfully applied to many fields, such as data mining, decision-making, pattern recognition, machine learning, and intelligent control [1][2][3][4]. Kernel rough sets [5] and neighborhood rough sets [6] are two important models in rough set theory.…”

Section: Introductionmentioning

confidence: 99%

Kernel Neighborhood Rough Sets Model and Its Application

Zeng

Jing

2018

Complexity

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Rough set theory has been successfully applied to many fields, such as data mining, pattern recognition, and machine learning. Kernel rough sets and neighborhood rough sets are two important models that differ in terms of granulation. The kernel rough sets model, which has fuzziness, is susceptible to noise in the decision system. The neighborhood rough sets model can handle noisy data well but cannot describe the fuzziness of the samples. In this study, we define a novel model called kernel neighborhood rough sets, which integrates the advantages of the neighborhood and kernel models. Moreover, the model is used in the problem of feature selection. The proposed method is tested on the UCI datasets. The results show that our model outperforms classic models.

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“…Entropy is a key measure for information. Since it is capable of quantifying the uncertainty of random variables and scaling the amount of information shared by them effectively, it has been widely used in many fields [ 6 , 14 ].…”

Section: Preliminariesmentioning

confidence: 99%

Feature Selection with Neighborhood Entropy-Based Cooperative Game Theory

Zeng

She

Niu

2014

Computational Intelligence and Neuroscience

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Feature selection plays an important role in machine learning and data mining. In recent years, various feature measurements have been proposed to select significant features from high-dimensional datasets. However, most traditional feature selection methods will ignore some features which have strong classification ability as a group but are weak as individuals. To deal with this problem, we redefine the redundancy, interdependence, and independence of features by using neighborhood entropy. Then the neighborhood entropy-based feature contribution is proposed under the framework of cooperative game. The evaluative criteria of features can be formalized as the product of contribution and other classical feature measures. Finally, the proposed method is tested on several UCI datasets. The results show that neighborhood entropy-based cooperative game theory model (NECGT) yield better performance than classical ones.

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Multi-Granulation Entropy and Its Applications

Cited by 18 publications

References 20 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

Kernel Neighborhood Rough Sets Model and Its Application

Feature Selection with Neighborhood Entropy-Based Cooperative Game Theory

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