Behavioural features for mushroom classification

• Main Authors: Ismail, Shuhaida, Zainal, Amy Rosshaida, Mustapha, Aida
• Format: Article
• Language: English
• Published: IEEE Xplore 2018
• Subjects: TA329-348 Engineering mathematics. Engineering analysis
• Online Access: http://eprints.uthm.edu.my/6064/1/AJ%202018%20%281016%29%20Behavioural%20features%20for%20mushroom%20classification.pdf; http://eprints.uthm.edu.my/6064/

Mushrooms have high benefits in the human body. However, not all mushrooms are edible. While some have medical properties to cure cancer, some other types of mushrooms may contain viruses that carry infectious diseases. This paper is set to study mushroom behavioural features such as the shape, surface and colour of the cap, gill and stalk, as well as the odour, population and habitat of the mushrooms. The Principal Component Analysis (PCA) algorithm is used for selecting the best features for the classification experiment using Decision Tree (DT) algorithm. The classification accuracy, coefficient metric, and time taken to build a classification model on a standard Mushroom dataset were measured. The behavioural feature of ‘odour’ was selected as the highest ranked feature that contribute to the high classification accuracy.