Open AccessAn Experimental Analysis on Rough Set Mean, Median, Mode Method of Dependency Values for Feature Selection in Medical Databases
Author(s) -
Swagata Devi,
V. Sasirekha
Publication year - 2019
Publication title -
asian journal of computer science and technology
Language(s) - English
Resource type - Journals
eISSN - 2583-7907
pISSN - 2249-0701
DOI - 10.51983/ajcst-2019.8.s1.1940
Subject(s) - rough set , reduct , imperfect , dependency (uml) , computer science , feature selection , data mining , set (abstract data type) , selection (genetic algorithm) , artificial intelligence , feature (linguistics) , measure (data warehouse) , algorithm , philosophy , linguistics , programming language
The problem of imperfect knowledge has been tackled for a long time by philosophers, logicians and mathematicians. Recently it became an important issue for scientists, particularly in the area of Artificial Intelligence. Their square measure several approaches to the matter of the way to perceive and manipulate imperfect information. The most successful approach is based on the rough set notion proposed by Z. Pawlak in the article [1]. The proposed method to find the quick reduct in medical data set using the roughest theory. This method has applied in many classification algorithms and find the measures to calculate the accuracy of this proposed method.