An Effective Rough Neutrosophic Based Approach for Data Pre-Processing

The core challenge in the process of knowledge discovery is pre-processing data. Pre-processing data involves feature selection due to substantial amount and degree dimension attribute. concept attribute reduction introduced by Pawlak rough set theory for computational efficiency accuracy. This helps eliminate unnecessary attributes. Rough neutrosophic an extension traditional hybrid theory. paper novel approach integrating reduce dependency. formal principle using indiscernibility relation. Besides, we proposed effective selecting features. For purpose gaining a better understanding method, algorithm heuristic constructed pre-processing. It contains seven steps distributed jobs produce final output which reduct. result shows impact on attributes, especially term dependency