Robust feature selection using label enhancement and β-precision fuzzy rough sets for multilabel fuzzy decision system

Yin, Tengyu; Chen, Hongmei; Li, Tianrui; Yuan, Zhong; Luo, Chuan

doi:10.1016/j.fss.2022.12.018

Cited by 14 publications

(1 citation statement)

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“…FRS can be effectively used for mitigating the effects of information loss as a consequence of discretization of features by using fuzzy similarity measures to tackle the continuous feature values 28 . Broadly, FRS aided dimensionality reduction 29 methods can be categorized into two types 30 , 31 which are based on discernibility matrix and dependency function 32 . Discernibility matrix assisted approaches provide numerous reduct sets 33 , whilst dependency function leads to a single feature subset 34 .…”

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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