Behavioural features for mushroom classification

Ismail, Shuhaida and Zainal, Amy Rosshaida and Mustapha, Aida (2018) Behavioural features for mushroom classification. IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE). pp. 412-415. ISSN 9781538635278

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Classification; Principal Component Analysis; Decision Tree; Data Mining.
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA329-348 Engineering mathematics. Engineering analysis
Divisions:	Faculty of Applied Science and Technology > Department of Mathematics and Statistics
Depositing User:	UiTM Student Praktikal
Date Deposited:	26 Jan 2022 04:26
Last Modified:	26 Jan 2022 04:26
URI:	http://eprints.uthm.edu.my/id/eprint/6064

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