Rafia Sharmin Alice

Work place: Computer Science and Engineering Discipline, Khulna University, Khulna, Bangladesh

E-mail: awesomealice91@gmail.com


Research Interests: Data Mining, Computer Graphics and Visualization, Computer Architecture and Organization, Pattern Recognition, Computer Vision


Rafia Sharmin Alice is a student at the Computer Science and Engineering Discipline, Khulna University, Bangladesh. She received her B.Sc. Engg. degree from Khulna University. Her research interests include Data Mining, Pattern Recognition, and Computer Vision.

Author Articles
Edibility Detection of Mushroom Using Ensemble Methods

By Nusrat Jahan Pinky S.M. Mohidul Islam Rafia Sharmin Alice

DOI: https://doi.org/10.5815/ijigsp.2019.04.05, Pub. Date: 8 Apr. 2019

Mushrooms are the most familiar delicious food which is cholesterol free as well as rich in vitamins and minerals. Though nearly 45,000 species of mushrooms have been known throughout the world, most of them are poisonous and few are lethally poisonous. Identifying edible or poisonous mushroom through the naked eye is quite difficult. Even there is no easy rule for edibility identification using machine learning methods that work for all types of data. Our aim is to find a robust method for identifying mushrooms edibility with better performance than existing works. In this paper, three ensemble methods are used to detect the edibility of mushrooms: Bagging, Boosting, and random forest. By using the most significant features, five feature sets are made for making five base models of each ensemble method. The accuracy is measured for ensemble methods using five both fixed feature set-based models and randomly selected feature set based models, for two types of test sets. The result shows that better performance is obtained for methods made of fixed feature sets-based models than randomly selected feature set-based models. The highest accuracy is obtained for the proposed model-based random forest for both test sets.

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