Gain ratio in weighting attributes on simple additive weighting

2nd Nommensen International Conference on Technology and Engineering

Rizka, Ade^1 ; Efendi, Syahril^2 ; Sirait, Pahala^3

Stud. in Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan, Indonesia^1

Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan, Indonesia^2

Faculty of Master Information Technology, STMIK STIE Mikroskil, Medan, Indonesia^3

关键词: Attribute weighting;    Decision making process;    Multi attribute decision making methods;    Relative standard deviations;    Simple additive weighting;    UCI machine learning repository;    Weighted linear combinations;    Weighting attributes;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/420/1/012099/pdf
DOI  :  10.1088/1757-899X/420/1/012099

来源: IOP

【 摘 要 】

Simple Additive Weighting (SAW) is one of Multi Attribute Decision Making (MADM) method known as simple weighted linear combination and most used. However, based on several studies, it produces lower accuracy values than other MADM methods. Because there is no validation in the weighting system for each attribute so that it affects the decision-making process and for some newly incompatible attributes causing errors in decision making and determining the best alternative. In this study, researchers used gain ratio as the basis of attribute weighting on SAW. Datasets used from UCI machine learning repository, such as cryotherapy, immunotherapy, ILPD and user knowledge modelling. The accuracy result of this research will be compare with the result of SAW method accuracy value based on the weight of the dataset using relative standard deviation. The average value of accuracy obtained by weighting attributes based on the weight of the dataset of 28.1825% and weight gain ratio of 31.6975%. Then on attribute weighting based on the gain ratio has a better accuracy. However, the Cryotherapy dataset value accuracy based on the weight gain ratio more 0.5% lower than the weight of the dataset due to the value in the spread dataset.