Using Fuzzy Logic for Data Aggregation in Vehicular Networks

Tal, Irina; Muntean, Gabriel‐Miro

doi:10.1109/ds-rt.2012.29

Cited by 5 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, the aggregation method used for such datasets should be unbiased, computationally efficient and should not suffer from problems, such as disjoint ranking and tied ranking. To cope with this need, we propose the idea of aggregating filtering metrics using fuzzy systems that are flexible and extensible (Tal and Muntean 2012). Moreover, the use of fuzzy systems for rank aggregation is also not computationally complex.…”

Section: Related Workmentioning

confidence: 99%

A hybrid feature selection model based on improved squirrel search algorithm and rank aggregation using fuzzy techniques for biomedical data classification

Nagarajan

Babu

2021

Netw Model Anal Health Inform Bioinforma

View full text Add to dashboard Cite

Feature selection has gained its importance due to the voluminous nature of the data. Owing to the computational complexity of wrapper approaches, the poor performance of filtering techniques, and the classifier dependency of embedded approaches, hybrid approaches are more commonly used in feature selection. Hybrid approaches use filtering metrics to reduce the computational complexity of wrapper algorithms and are proved to yield better feature subset. Though filtering metrics select the features based on their significance, most of them are unstable and biased towards the metric used. Moreover, the choice of filtering metrics depends largely on the distribution of data and data types. Biomedical datasets contain features with different distribution and types adding to the complexity in the choice of filtering metric. We address this problem by proposing a stable filtering method based on rank aggregation in hybrid feature selection model with Improved Squirrel search algorithm for biomedical datasets. Our proposed model is compared with other well-known and state-of-the-art methods and the results prove that our model exhibited superior performance in terms of classification accuracy and computational time. The robustness of our proposed model is proved by conducting experiments on nine biomedical datasets and with three different classifiers.

show abstract

Section: Related Workmentioning

confidence: 99%