Exploiting fuzzy rough mutual information for feature selection

Wang, Zhihong; Chen, Hongmei; Yuan, Zhong; Yang, Xiaoling; Zhang, Pengfei; Li, Tianrui

doi:10.1016/j.asoc.2022.109769

Cited by 15 publications

(3 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Entire reduction process is accomplished over complete data by using both fuzzy and IF aided techniques. FSFrMI 72 , GIFRFS 57 , TIFRFS 59 , and FRFS 6 are the earlier efficacious and effective techniques, which are incorporated to perform the comparative results (Table 2 ). Our proposed method produced reduct set range from 7 to 169, where reduct size is smaller when compared to reduct size by earlier approaches, except bank marketing and thyroid-hypothyroid datasets.…”

Section: Resultsmentioning

confidence: 99%

“…Further, it can be stated that mutual information (MI) 71 is an interesting quantity that evaluates the dependence between conditional features and has been repeatedly employed to solve an extensive diverse problems. Feature selection techniques can be converted into effective one by incorporating information entropy estimation notion for attribute extraction based on MI 72 and the conventional feature selection approaches on the basis of class seperability. Broadly MI measures the amount of information that can be deduced from a random variable/vector about another random variable/vector 73 , 74 .…”

Section: Introductionmentioning

confidence: 99%

“…It deduces the target MI with minimum redundancy 10 , 77 among the selected features. A number of MI based feature selection algorithms have been in practice in various domains 72 , 74 . Fuzzy rough entropy was effectively used to avoid the limitation of rough entropy to handle the real-valued feature data 78 , 79 , but fuzzy rough entropy leads to lessening monotonically with the rise of the dimensions of data, which can promptly reflect the roughness of information systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

View full text Add to dashboard Cite

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.

show abstract