Prosiectau fesul blwyddyn
In the context of big data, granular computing has recently been implemented by some mathematical tools, especially Rough Set Theory (RST). As a key topic of rough set theory, feature selection has been investigated to adapt the related granular concepts of RST to deal with large amounts of data, leading to the development of the distributed RST version. However, despite of its scalability, the distributed RST version faces a key challenge tied to the partitioning of the feature search space in the distributed environment while guaranteeing data dependency. Therefore, in this manuscript, we propose a new distributed RST version based on Locality Sensitive Hashing (LSH), named LSH-dRST, for big data feature selection. LSH-dRST uses LSH to match similar features into the same bucket and maps the generated buckets into partitions to enable the splitting of the universe in a more efficient way. More precisely, in this paper, we perform a detailed analysis of the performance of LSH-dRST by comparing it to the standard distributed RST version, which is based on a random partitioning of the universe. We demonstrate that our LSH-dRST is scalable when dealing with large amounts of data. We also demonstrate that LSH-dRST ensures the partitioning of the high dimensional feature search space in a more reliable way; hence better preserving data dependency in the distributed environment and ensuring a lower computational cost.
Ôl bysGweld gwybodaeth am bynciau ymchwil 'A Detailed Study of the Distributed Rough Set Based Locality Sensitive Hashing Feature Selection Technique'. Gyda’i gilydd, maen nhw’n ffurfio ôl bys unigryw.
- 1 Wedi Gorffen
Optimised Framework based on Rough Set Theory for Big Data Pre-processing in Certain and Imprecise Contexts - RoSTBiDFramework
01 Maw 2017 → 28 Chwef 2019
Prosiect: Ymchwil a ariannwyd yn allanol