TY - JOUR ID - 142085 TI - Rough sets theory and its extensions for attribute reduction: a review JO - Big Data and Computing Visions JA - BDCV LA - en SN - 2783-4956 AU - Eskandari, Sadegh AD - Department of Computer Science, University of Guilan, Rasht, Iran. Y1 - 2021 PY - 2021 VL - 1 IS - 2 SP - 96 EP - 100 KW - Rough Set Theory KW - data science KW - Data set DO - 10.22105/bdcv.2021.142085 N2 - The rough sets theory is a mathematical tool to express vagueness by means of boundary region of a set. The main advantage of this implementation of vagueness is that it requires no human input or domain knowledge other than the given data set. Several efforts have been made to make close the rough sets theory and machine learning tasks. In this regard several extensions and modifications of the original theory are proposed. This paper provides the basic concepts of the theory as well as its well-known extensions and modifications. UR - https://www.bidacv.com/article_142085.html L1 - https://www.bidacv.com/article_142085_eb1f70ca734778c2b84efe1859a862d9.pdf ER -