Abstract
This is the era of information and the amount of data has immensely increased since the last few years. This increase has resulted in dilemmas like the curse of dimensionality where we need a large number of resources to process the huge number of features. So far there are many tools available that process such large volumes of data among which Rough Set Theory is a prominent one. It provides the concept of Reducts which represent the set of attributes that provide the maximum of the information. However, the problem with Reducts is that they are not stable and keep on changing with the addition of more and more data. So, the concept of Dynamic Reducts is introduced in the literature to provide a more stable version of Reducts. There are many Dynamic Reduct finding algorithms available; however, these dynamic Reduct finding algorithms are computationally too expensive and inefficient for large datasets. In this research, we have presented an improved dynamic Reduct finding technique based on rough set theory. In this technique, Reducts are selected, optimized, and further generalized through Parallel Feature Sampling (PFS) algorithm. An in-depth investigation is performed using various benchmark datasets to justify the effectiveness of our proposed approach. Results have shown that the proposed approach outperforms the state-of-the-art approaches in terms of both efficiency and effectiveness. Overall, 96% average accuracy is achieved and 46.13% reduction in execution time is observed by the proposed algorithm against the compared contemporary approaches.