TY  - JOUR
TT  - The Classification of White Wine and Red Wine According to Their Physicochemical Qualities
AU  - Er, Yeşim
AU  - Atasoy, Ayten
PY  - 2016
DA  - December
DO  - 10.18201/ijisae.265954
JF  - International Journal of Intelligent Systems and Applications in Engineering
PB  - İsmail SARITAŞ
WT  - DergiPark
SN  - 2147-6799
SP  - 23
EP  - 26
VL  - 4
IS  - Special Issue-1
KW  - Classification
KW  - Random Forests
KW  - Support Vector Machines
KW  - k Nearest Neighbourhood
N2  - The main purpose of this study is to predict wine quality based onphysicochemical data. In this study, two large separate data sets which weretaken from UC Irvine Machine Learning Repository were used. These data setscontain 1599 instances for red wine and 4898 instances for white wine with 11features of physicochemical data such as alcohol, chlorides, density, totalsulfur dioxide, free sulfur dioxide, residual sugar, and pH. First, theinstances were successfully classified as red wine and white wine with theaccuracy of 99.5229% by using Random Forests Algorithm. Then, the followingthree different data mining algorithms were used to classify the quality ofboth red wine and white wine: k-nearest-neighbourhood, random forests andsupport vector machines. There are 6 quality classes of red wine and 7 qualityclasses of white wine. The most successful classification was obtained by usingRandom Forests Algorithm. In this study, it is also observed that the use ofprincipal component analysis in the feature selection increases the successrate of classification in Random Forests Algorithm.
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UR  - https://doi.org/10.18201/ijisae.265954
L1  - https://dergipark.org.tr/en/download/article-file/232398
ER  -