Mushroom Classification Using Machine Learning

This study evaluated three machine learning algorithms for automated mushroom classification. Experimental results indicate that Support Vector Machine achieves the highest accuracy at 100.00%, outperforming both Logistic Regression (94.65%) and Naïve Bayes (92.18%). Furthermore, SVM demonstrated superior computational efficiency, processing classification tasks faster than comparative algorithms. These findings suggest that SVM is a reliable and efficient approach for automated edible/poisonous mushroom identification, potentially assisting non-experts in making safe consumption decisions. Future work may explore ensemble methods, deep learning architectures, and cross-dataset validation to enhance generalization capabilities.

[1]   Boa, E.R., Wild Edible Fungi: A Global Overview of Their Use and Importance toPeople. Rome: Food and Agriculture Organization of the United Nations, 2004. [2]   Kousalya, K., et al., "Edible mushroom identification using machine learning," in Proc.2022 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 2022, pp. 1-6. [3]   Ismail, S., A.R. Zainal, and A. Mustapha, "Behavioural features for mushroomclassification," in Proc. 2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), Penang, Malaysia, 2018, pp. 1-6. [4]   Mahesh, B., "Machine learning algorithms—a review," International Journal of Scienceand Research (IJSR), vol. 9, pp. 381-386, 2020. [5]   Ottom, M.A., N.A. Alawad, and K. Nahar, "Classification of mushroom fungi usingmachine learning techniques," International Journal of Advanced Trends in Computer Science and Engineering, vol. 8, no. 5, pp. 2378-2385, 2019. [6]   Hamonangan, R., M.B. Saputro, and C.B.S.D.K. Atmaja, "Accuracy of classificationpoisonous or edible of mushroom using naïve bayes and k-nearest neighbors," Journal of Soft Computing Exploration, vol. 2, no. 1, pp. 53-60, 2021. [7]   Halili, F. and F. Kamberi, "Performance analysis of classification algorithms: a casestudy of Naïve Bayes and J48 in Big Data," Applied Mathematics and Computation, vol. 2, no. 2, pp. 50-57, 2021.