Classification algorithm for edible mushroom identification

Wibowo, Agung Surya; Rahayu, Yuri; Riyanto, Andi; Hidayatulloh, Taufik

doi:10.1109/icoiact.2018.8350746

Cited by 34 publications

(13 citation statements)

References 2 publications

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“…Odor feature of mushroom features was identified as the one with the highest ranking. In [20] performance of three classifiers: decision tree (DT), NaïveBayes and support vector machine (SVM) for UCI machine learning mushroom dataset is performed. Both Decision tree and SVM resulted in cent percent accuracy, however, it was observed that SVM took more processing time compared DT.…”

Section: Related Workmentioning

confidence: 99%

Mushroom Edibility Identification Applying CBR and Ant Lion Techniques in Multi-sensor Environment

Devika

Ramesh

Karegowda³

2021

SN COMPUT. SCI.

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Wireless sensor networks (WSN) are part of our daily life as they play a vital role in applications of various domains. Energy optimization is a major challenge of WSN as they are operated through the battery. Data exchange is a significant and often performed operation in any WSN which has to be operated with less energy consumption. Bio-inspired clustering protocols are proving a success in minimizing energy consumption and are of current research interest by many researchers. This paper briefs on work carried on the classification of mushroom into edible or non-edible. Since most mushrooms are dangerous to health and may lead to death, henceforth is it is essential to identify the edibility of mushroom. Mushroom features identified by sensor such as ring, odur, spore_print_color, stalk_color_above, stalk_surface_above, and gill size are forwarded to base station (BS). Ant Lion optimization clustering algorithm (ALOC) is adopted in the routing of information to BS. ALOC avoids improper clustering reducing multiple messages at BS. The work is divided into two phases in the initial phase sensing of mushroom features and forwarding to BS is performed through WSN. In the second phase, the decision is taken whether mushroom is edible or not applying class-based association rules (CBA). The simulation results show ALOC is better than LEACH, ABC PSO and MLEACH through evaluation results in terms of network lifetime, energy consumption and retained alive nodes. correlation-based feature selection (CFS) with three filter search techniques: genetic algorithm (GA), evolutionary algorithm (EA), and particle swarm optimization (PSO) at BS as an evaluation method for selection of significant feature selection. CBA rules were generated using these subsets of significant features; hence resulted in a limited number of strong rules which are reliable and sufficient enough to classify the mushroom as edible or not. The patterns and rules generated using the proposed approach avoid the generation of duplicate and irrelevant rules and henceforth simplifies the analysis process using a reliable and interesting set of rules.

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Section: Related Workmentioning

confidence: 99%

Mushroom Edibility Identification Applying CBR and Ant Lion Techniques in Multi-sensor Environment

Devika

Ramesh

Karegowda³

2021

SN COMPUT. SCI.

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“…Agung Wibowo et al [8] compared the performance among three data mining algorithms: C4.5 based decision Tree, Naï ve Bayes, and SVM (Support Vector Machines). For performing the experiment, data set is taken from Audubon Society Field Guide to North American Mushrooms, available in the UCI machine learning repository [13] which includes Agaricus and Lepiota families of mushroom.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, C4.5 is considered as the best among these three algorithms. In addition, C4.5 discard 5 from 22 attributes and classify based on these five attributes which are the odor, sporeprint-color, gill-size, gill-spacing, and population [8] .…”

Section: Related Workmentioning

confidence: 99%

“…For classifying mushroom, a narrow range of studies has been done using classification. [5,[7][8][9][10][11] are some of them where Decision Tree (C4.5), SVM (Support Vector Machine), ANN, ANFIS Naï ve Bayes, Bayes Net, ZeroR, and RIDOR single classifier algorithms have been used to classify mushroom. But the performance of single classifier algorithms is poorer than ensemble methods.…”

Section: Introductionmentioning

confidence: 99%

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Edibility Detection of Mushroom Using Ensemble Methods

Pinky¹,

Islam²,

Alice³

2019

IJIGSP

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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 modelbased random forest for both test sets.

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“…Thus, it is found that the result obtained by using the decision tree produces the best results of classifying the edible and the poisonous mushrooms. (Wibowo, A et al, 2018) say that mushrooms can be classified into poisonous and edible using machine learning and data mining techniques. The classification algorithms such as naïve bayes, decision Tree (C4.5) and Support Vector Machine (SVM) have been used for classification and the experiment is performed with the help of Weka.…”

Section: Introductionmentioning

confidence: 99%

Classification of Mushrooms to Detect their Edibility Based on Key Attributes

Vanitha¹

2020

Biosci. Biotech. Res. Comm

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Classification algorithm for edible mushroom identification

Cited by 34 publications

References 2 publications

Mushroom Edibility Identification Applying CBR and Ant Lion Techniques in Multi-sensor Environment

Mushroom Edibility Identification Applying CBR and Ant Lion Techniques in Multi-sensor Environment

Edibility Detection of Mushroom Using Ensemble Methods

Classification of Mushrooms to Detect their Edibility Based on Key Attributes

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