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

Edibility Detection of Mushroom Using Ensemble Methods

Full Text (PDF, 1114KB), PP.55-62

Views: 0 Downloads: 0

Author(s)

Nusrat Jahan Pinky ^1,* S.M. Mohidul Islam ¹ Rafia Sharmin Alice ¹

1. Computer Science and Engineering Discipline, Khulna University, Khulna, Bangladesh

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2019.04.05

Received: 10 Mar. 2019 / Revised: 19 Mar. 2019 / Accepted: 28 Mar. 2019 / Published: 8 Apr. 2019

Index Terms

Fixed Feature Set, Randomly Selected Feature Set, Base Classifier, Bagging, Boosting, Random Forest

Abstract

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.

Cite This Paper

Nusrat Jahan Pinky, S.M. Mohidul Islam, Rafia Sharmin Alice, " Edibility Detection of Mushroom Using Ensemble Methods", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.4, pp. 55-62, 2019. DOI: 10.5815/ijigsp.2019.04.05

Reference

[1] M. E. Valverde, T. Hernández-pérez, and O. Paredeslópez, “Review Article Edible Mushrooms : Improving Human Health and Promoting Edible Mushrooms : Improving Human Health and Promoting,” no. January, 2015.

[2] Mercola.com. (2019). The Mushroom Advantage: Benefits and Uses of Mushroom. [online] Available at: https://www.mercola.com/infographics/mushrooms.htm [Accessed 23 Jan. 2019].

[3] Md Mahabub Alam, Md Waliul Bari, ‘Investment in Mushroom Cultivation at Savar Upazila: A Prospective Sector for Bangladesh’, ASA University Review, Vol. 4 No. 2, July–December, 2010

[4] ‘Mushroom’,http://en.banglapedia.org/index.php?title=Mushroom,Accessed Date: 01 August, 2018

[5] D. R. Chowdhury and S. Ojha, “An Empirical Study on Mushroom Disease Diagnosis: A Data Mining Approach,” 2017.

[6] C. FM, “Amanita phalloides in Victoria,” p. 849–850., 1993.

[7] A. R. Khan, S. S. Nisha, and M. M. Sathik, “Clustering Techniques For Mushroom Dataset,” no. June, pp. 1121–1125, 2018.

[8] Agung Wibowo, Yuri Rahayu, Andi Riyanto, and Taufik Hidayatulloh. "Classification algorithm for edible mushroom identification." In Information and Communications Technology (ICOIACT), 2018 International Conference on, pp. 250-253. IEEE, 2018.

[9] Beniwal, Sunita and Bishan Das. “Mushroom Classification Using Data Mining Techniques.” International Journal of Pharma and Bio Sciences, Vol 6, issue 1, pp. 1170-1176, 2015.

[10] Lavanya, B. “Performance Analysis of Decision Tree Algorithms on Mushroom Dataset.” International Journal for Research in Applied Science and Engineering Technology. Vol. 5, issue XI, pp. 183-191, 2017.

[11] S.K. Verma, M. Dutta, “Mushroom Classification Using ANN & ANFIS Algorithm”, IOSR Journal of Engineering (IOSRJEN), Vol. 08, Issue 01, PP 94-100, January. 2018.

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

[13] “UCI Machine Learning Repository: Mushroom Data Set.” [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Mushroom.

[14] Eusebi, Clara, Cosmin Gliga, Deepa John, and Andre Maisonave. "Data Mining on Mushroom Database." Journal of CSIS, Pace University (2008): 1-9.

[15] “What is the difference between Bagging and Boosting?” Quantdare,. [Online]. Available: https://quantdare.com/what-is-the-difference-between-bagging-and-boosting/.

[16] “What is the difference between Bagging, Random Forest and Boosted Tree? Which should I use?” [Online]. Available: https://support.bigml.com/hc/en-us/articles/206739539-What-is-the-difference-between-Bagging-Random-Decision-Forest-and-Boosted-Tree-Which-one-should-I-use-

[17] J. Han, M. Kamber, J. Pei. “Data Mining Concepts & Techniques.”, Morgan Kaufmann Publishers, ELSEVIER, 3rd edition.

[18] “AdaBoost,”. [Online]. Available: https://infinitescript.com/2016/09/adaboost/ .

[19] Anon, (2019). [online] Available at: https://towardsdatascience.com/decision-trees-in-machine-learning-641b9c4e8052. [Accessed 10 Jan. 2019].

[20] “Ensemble learning, beginners guide, machine learning, data science, analytics,” [Online]. Available: https://www.analyticsvidhya.com/blog/2015/08/introduction-ensemble-learning/.

International Journal of Image, Graphics and Signal Processing (IJIGSP)