A Neighborhood Rough Sets-Based Attribute Reduction Method Using Lebesgue and Entropy Measures

Sun, Lin; Wang, Lanying; Xu, Jiucheng; Zhang, Shiguang

doi:10.3390/e21020138

Cited by 21 publications

(19 citation statements)

References 56 publications

(104 reference statements)

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“…The values of F F are displayed in Table 30. The critical value of F (5,20) for α = 0.10 is 3.21, and the null hypothesis at α = 0.10 will be rejected. In addition, for the Bonferroni-Dunn test, q α = 2.326 is calculated at a significance level of α = 0.10 and CD = 2.752 (s = 5 and T = 6).…”

Section: G Statistical Analysismentioning

confidence: 96%

“…Improved technologies have been developed and rapid progress has been made in feature selection for multilabel data [2], [3]. Feature selection models can be categorized as filter, wrapper and embedded methods [4], [5]. Filter methods select features according to the intrinsic properties of datasets, such as the distance, dependency, and information gain [6].…”

Section: With the Development Of Data Processing Technologies In Machmentioning

confidence: 99%

“…For realizing efficient feature reduction, many heuristic search algorithms have been investigated, and the forward greedy search method is typically employed [4], [5], [20], [22]. In the following, based on the preceding NCFS-BPSO algorithm, the second part designs a multilabel feature selection algorithm based on dependency degree with the neighborhood approximation accuracy (MFSDNA), which is presented as Algorithm 2.…”

Section: Algorithm 1 Ncfs-bpso Algorithmmentioning

confidence: 99%

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Hybrid Multilabel Feature Selection Using BPSO and Neighborhood Rough Sets for Multilabel Neighborhood Decision Systems

et al. 2019

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Recently, feature selection for multilabel classification has attracted substantial attention in many fields; however, some of the available methods ignore the correlations among labels and yield low classification performance. In addition, most feature selection algorithms that are based on multilabel neighborhood rough sets (MNRS) can only deal with finite sets for multilabel data. To address the issues, this paper presents a novel hybrid filter-wrapper multilabel feature selection method that is based on binary particle swarm optimization (BPSO) and MNRS with the Lebesgue measure for multilabel neighborhood decision systems. First, to overcome the problem that the traditional correlation-based feature selection (CFS) algorithm ignores the dependencies among labels, two types of average correlation between single labels and label sets and among labels are presented. Via combination with information-entropy-based uncertainty measures, a new average correlation among labels is studied. A novel comprehensive evaluation function of CFS (NCFS) is constructed. Then, NCFS is introduced as a fitness function into the original BPSO and improved BPSO algorithms to optimize multilabel classification in the early and later stages, respectively, and the optimization process is terminated when the maximum number of iterations is reached. Next, the Lebesgue measure of the neighborhood class is developed for investigating the neighborhood approximation accuracy and the dependency degree based on MNRS. Various properties are deduced, and the relationships among these measures are used to evaluate the uncertainty and correlations among labels of multilabel data. Finally, a hybrid filter-wrapper feature selection algorithm using NCFS-BPSO is designed for preliminarily eliminating redundant features to decrease the complexity, and a heuristic forward multilabel feature selection algorithm is proposed for improving the performance of multilabel classification. Experimental results on fifteen multilabel datasets demonstrate that our proposed algorithms are effective in selecting significant features and realizing great classification performance in multilabel neighborhood decision systems.

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Section: G Statistical Analysismentioning

confidence: 96%

Section: With the Development Of Data Processing Technologies In Machmentioning

confidence: 99%

Section: Algorithm 1 Ncfs-bpso Algorithmmentioning

confidence: 99%

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Hybrid Multilabel Feature Selection Using BPSO and Neighborhood Rough Sets for Multilabel Neighborhood Decision Systems

et al. 2019

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“…(1) Leukemia1 dataset consists of 7129 genes and 72 samples with two subtypes: patients and healthy people (Sun et al, 2019a). (2) Leukemia2 dataset consists of 5327 genes and 72 samples with three subtypes: ALL-T (acute lymphoblastic leukemia, T-cell), ALL-B (acute lymphoblastic leukemia, B-cell), and AML (acute myeloid leukemia) (Dong et al, 2018).…”

Section: Gene Expression Data Setsmentioning

confidence: 99%

Feature Selection Using Approximate Conditional Entropy Based on Fuzzy Information Granule for Gene Expression Data Classification

Zhang

2021

Front. Genet.

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Classification is widely used in gene expression data analysis. Feature selection is usually performed before classification because of the large number of genes and the small sample size in gene expression data. In this article, a novel feature selection algorithm using approximate conditional entropy based on fuzzy information granule is proposed, and the correctness of the method is proved by the monotonicity of entropy. Firstly, the fuzzy relation matrix is established by Laplacian kernel. Secondly, the approximately equal relation on fuzzy sets is defined. And then, the approximate conditional entropy based on fuzzy information granule and the importance of internal attributes are defined. Approximate conditional entropy can measure the uncertainty of knowledge from two different perspectives of information and algebra theory. Finally, the greedy algorithm based on the approximate conditional entropy is designed for feature selection. Experimental results for six large-scale gene datasets show that our algorithm not only greatly reduces the dimension of the gene datasets, but also is superior to five state-of-the-art algorithms in terms of classification accuracy.

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“…Even thousands of attributes may be acquired in some real-world databases. In order to shorten the processing time and obtain better generalization, the attribute reduction problem attracts more and more attention in recent years [ 5 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

An Attribute Reduction Method using Neighborhood Entropy Measures in Neighborhood Rough Sets

Sun

Zhang

et al. 2019

Entropy

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Attribute reduction as an important preprocessing step for data mining, and has become a hot research topic in rough set theory. Neighborhood rough set theory can overcome the shortcoming that classical rough set theory may lose some useful information in the process of discretization for continuous-valued data sets. In this paper, to improve the classification performance of complex data, a novel attribute reduction method using neighborhood entropy measures, combining algebra view with information view, in neighborhood rough sets is proposed, which has the ability of dealing with continuous data whilst maintaining the classification information of original attributes. First, to efficiently analyze the uncertainty of knowledge in neighborhood rough sets, by combining neighborhood approximate precision with neighborhood entropy, a new average neighborhood entropy, based on the strong complementarity between the algebra definition of attribute significance and the definition of information view, is presented. Then, a concept of decision neighborhood entropy is investigated for handling the uncertainty and noisiness of neighborhood decision systems, which integrates the credibility degree with the coverage degree of neighborhood decision systems to fully reflect the decision ability of attributes. Moreover, some of their properties are derived and the relationships among these measures are established, which helps to understand the essence of knowledge content and the uncertainty of neighborhood decision systems. Finally, a heuristic attribute reduction algorithm is proposed to improve the classification performance of complex data sets. The experimental results under an instance and several public data sets demonstrate that the proposed method is very effective for selecting the most relevant attributes with great classification performance.

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A Neighborhood Rough Sets-Based Attribute Reduction Method Using Lebesgue and Entropy Measures

Cited by 21 publications

References 56 publications

Hybrid Multilabel Feature Selection Using BPSO and Neighborhood Rough Sets for Multilabel Neighborhood Decision Systems

Hybrid Multilabel Feature Selection Using BPSO and Neighborhood Rough Sets for Multilabel Neighborhood Decision Systems

Feature Selection Using Approximate Conditional Entropy Based on Fuzzy Information Granule for Gene Expression Data Classification

An Attribute Reduction Method using Neighborhood Entropy Measures in Neighborhood Rough Sets

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