چکیده مقاله
Classification in machine learning is done bymany factors which called attributes The higher the numberof features, the more difficult it becomes to visualize thetraining set and then work on it Sometimes, most of thesefeatures are related to each other and are therefore consideredredundant features This is where Dimensionality Reduction DR algorithms come into play In machine learning andstatistics, dimensionality reduction is the process of reducingthe number of supervised random variables by obtaining a setof main variables Dimensionality reduction can be divided intofeature selection and feature extraction This paper proposes anew Dimensionality reduction algorithm in the featureselection category using Pearson correlation of attributes andmaking uncertain graph models The proposed model can bedone for any number of features with increasing theclassification performance compared with filter and wrapperstrategies
کلیدواژهها
نویسندگان
Arezoo Jahani
Faculty of Electrical Engineering,Sahand University of Technlogy,Tabriz, Iran
شیوه ارجاع
Jahani, Arezoo,1401,Dimensionality Reduction based on UncertainGraph Model,1st International Conference and 6th National Conference on Computers, information technology and applications of artificial intelligence
ارائهشده در
مجموعه مقالات اولین کنفرانس بین المللی و ششمین کنفرانس ملی کامپیوتر، فناوری اطلاعات و کاربردهای هوش مصنوعی3 اسفند 1401 · اهواز