Scopus
Edible and Poisonous Mushrooms Classification by Machine Learning Algorithms
2022 11th Mediterranean Conference on Embedded Computing, MECO 2022 · Ocak 2022
Makale Bilgileri
Dergi2022 11th Mediterranean Conference on Embedded Computing, MECO 2022
Yayın TarihiOcak 2022
Scopus ID2-s2.0-85133964752
Özet
Of the millions of mushroom species growing all around the world, one type is edible, while the other is poisonous. It is not easy to distinguish edible and poisonous mushrooms from each other and it is a condition that requires expertise. The classification of poisonous and edible mushrooms is therefore important. Machine learning algorithms are an alternative method for classifying poisonous and edible mushrooms using morphological or physical features of fungi. The dataset used in this study is the Mushroom dataset available in the UC Irvine Machine Learning Repository. Based on 22 features in the Mushroom dataset and four different machine learning algorithms, models have been created for the classification of edible and poisonous fungi. The classification success rates of these models were obtained from Naive Bayes, Decision Tree, Support Vector Machine and AdaBoost algorithms with 90.99%, 98.82%, 99.98% and 100%, respectively. When these results were examined, taking into account the physical appearance features of the mushrooms, it was determined whether the mushrooms were edible and poisonous by 100% with the AdaBoost model.
Yazarlar (4)
1
Kemal Tutuncu
2
Ilkay Cinar
ORCID: 0000-0003-0611-3316
3
Ramazan Kursun
ORCID: 0000-0002-6729-1055
4
Murat Koklu
ORCID: 0000-0002-2737-2360
Anahtar Kelimeler
edible mushrooms
machine learning
mushroom dataset
poisonous mushrooms
UCI machine learning repository
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
9
Atıf
4
Yazar
5
Anahtar Kelime