A noise-aware fuzzy rough set approach for feature selection

Yang, Xiaoling; Wan, Jihong; Li, Tianrui; Luo, Chuan

doi:10.1016/j.knosys.2022.109092

Cited by 32 publications

(5 citation statements)

References 41 publications

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“…The feature selection technique [50,51] is one of the most important tasks for machine learning researchers. It helps to reduce the complexity of the learning models by removing redundant or spurious features.…”

Section: Feature Selectionmentioning

confidence: 99%

Rough-Fuzzy Based Synthetic Data Generation Exploring Boundary Region of Rough Sets to Handle Class Imbalance Problem

Naushin

Das

Nayak

et al. 2023

Axioms

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Class imbalance is a prevalent problem that not only reduces the performance of the machine learning techniques but also causes the lacking of the inherent complex characteristics of data. Though the researchers have proposed various ways to deal with the problem, they have yet to consider how to select a proper treatment, especially when uncertainty levels are high. Applying rough-fuzzy theory to the imbalanced data learning problem could be a promising research direction that generates the synthetic data and removes the outliers. The proposed work identifies the positive, boundary, and negative regions of the target set using the rough set theory and removes the objects in the negative region as outliers. It also explores the positive and boundary regions of the rough set by applying the fuzzy theory to generate the samples of the minority class and remove the samples of the majority class. Thus the proposed rough-fuzzy approach performs both oversampling and undersampling to handle the imbalanced class problem. The experimental results demonstrate that the novel technique allows qualitative and quantitative data handling.

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Section: Feature Selectionmentioning

confidence: 99%

Rough-Fuzzy Based Synthetic Data Generation Exploring Boundary Region of Rough Sets to Handle Class Imbalance Problem

Naushin

Das

Nayak

et al. 2023

Axioms

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“…Based on dependency function, we compute significance of a subset of features. Moreover, the conditional entropy measure is employed in to calculate reduct set for both homogeneous and heterogeneous information system respectively 36 – 38 . However, it may lead to misclassification of samples when there is a large degree of imbricate between diverse categories of data.…”

Section: Introductionmentioning

confidence: 99%

Hybrid similarity relation based mutual information for feature selection in intuitionistic fuzzy rough framework and its applications

Tiwari,

Saini,

Nath

et al. 2024

Sci Rep

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Fuzzy rough entropy established in the notion of fuzzy rough set theory, which has been effectively and efficiently applied for feature selection to handle the uncertainty in real-valued datasets. Further, Fuzzy rough mutual information has been presented by integrating information entropy with fuzzy rough set to measure the importance of features. However, none of the methods till date can handle noise, uncertainty and vagueness simultaneously due to both judgement and identification, which lead to degrade the overall performances of the learning algorithms with the increment in the number of mixed valued conditional features. In the current study, these issues are tackled by presenting a novel intuitionistic fuzzy (IF) assisted mutual information concept along with IF granular structure. Initially, a hybrid IF similarity relation is introduced. Based on this relation, an IF granular structure is introduced. Then, IF rough conditional and joint entropies are established. Further, mutual information based on these concepts are discussed. Next, mathematical theorems are proved to demonstrate the validity of the given notions. Thereafter, significance of the features subset is computed by using this mutual information, and corresponding feature selection is suggested to delete the irrelevant and redundant features. The current approach effectively handles noise and subsequent uncertainty in both nominal and mixed data (including both nominal and category variables). Moreover, comprehensive experimental performances are evaluated on real-valued benchmark datasets to demonstrate the practical validation and effectiveness of the addressed technique. Finally, an application of the proposed method is exhibited to improve the prediction of phospholipidosis positive molecules. RF(h2o) produces the most effective results till date based on our proposed methodology with sensitivity, accuracy, specificity, MCC, and AUC of 86.7%, 90.1%, 93.0% , 0.808, and 0.922 respectively.

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“…The rough set (RS) theory was proposed by the Polish mathematician Z. Pawlak in 1982 for superior knowledge simplification in the field of uncertainty and ambiguity [22]. Combining the RS algorithm with the conditional information entropy could overcome the sensitivity of RS to noise [23,24]. Considering that the conditional information entropy and rough set (CIERS) method is not sensitive to the information of the first-level indicator when the indicators are stratified, this paper proposes to optimize the CIERS method based on the PSO algorithm [25].…”

Section: Introductionmentioning

confidence: 99%

Condition Information Entropy and Rough Set Method Based on Particle Swarm Optimization Applied in the Natural Quality Evaluation of Cultivated Land

Yu,

Zhang

2024

Sustainability

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The evaluation of the natural quality of cultivated land is crucial for preserving arable land and achieving a balance between the quantity and quality of arable land. Therefore, a timely assessment of the natural quality of cultivated land is needed to monitor its changes. However, current methods often focus on a single specified crop, neglecting the variations that occur across different specified crops. Since the indicator weight recognition method is only suitable for a single crop, this paper proposes a novel model evaluating the natural quality of cultivated land based on the method of “hidden light–temperature index and yield ratio coefficient”. In addition, the condition information entropy and rough set method based on particle swarm optimization (CIERS-PSO) were proposed to evaluate the natural quality of cultivated land in Enshi. Firstly, condition information entropy and rough set are adopted to determine the importance of the indicator automatically. Then, particle swarm optimization (PSO) is utilized to obtain the optimal weights of the first-level and second-level indicators. Finally, the proposed model and evaluation method were adopted to evaluate the natural qualities of the cultivated land. The experimental results demonstrated that the combination of the “hidden light–temperature index and yield ratio coefficient” model and the CIERS-PSO method can automatically identify the indicator weights for the evaluation of natural quality in multi-crop cultivated land. It could obtain better evaluation accuracy even if the sample size is small.

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A noise-aware fuzzy rough set approach for feature selection

Cited by 32 publications

References 41 publications

Rough-Fuzzy Based Synthetic Data Generation Exploring Boundary Region of Rough Sets to Handle Class Imbalance Problem

Rough-Fuzzy Based Synthetic Data Generation Exploring Boundary Region of Rough Sets to Handle Class Imbalance Problem

Hybrid similarity relation based mutual information for feature selection in intuitionistic fuzzy rough framework and its applications

Condition Information Entropy and Rough Set Method Based on Particle Swarm Optimization Applied in the Natural Quality Evaluation of Cultivated Land

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