Classification algorithm for edible mushroom identification

Agung Wibowo, Yuri Rahayu, A. Riyanto, Taufik Hidayatulloh
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引用次数: 24

Abstract

Indonesia has 13% species of mushroom in the world but there is a very limited study on determining edible or poisonous mushroom. Classification process of poisonous mushroom or not will be easily conducted by learning machine using mining data as one of the ways to extract computer assisted knowledge. Currently, there are three comparisons of the best classification algorithms in data mining, namely: Decision Tree (C4.5), NaïveBayes and Support Vector Machine (SVM). The study method used is experiment with assisted tool of WEKA that has been testing in the comparison of the three algorithms. To conduct the testing, it is used the mushroom data of Agaricus and Lepiota family. The mushroom data were taken from The Audubon Society Field Guide to North American Mushrooms, in UCI machine learning repository. Results of the testing indicate that the C4.5 algorithm has the same accuracy level to the SVM by 100% however, from the speed aspect, process of the C4.5 algorithm is faster than the SVM.
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食用菌识别的分类算法
印度尼西亚拥有世界上13%的蘑菇品种,但在确定可食用或有毒蘑菇方面的研究非常有限。将数据挖掘作为计算机辅助知识提取的一种方式,学习机可以很容易地对毒蕈进行分类。目前,比较数据挖掘中最好的分类算法有三种,分别是:决策树(C4.5)、NaïveBayes和支持向量机(SVM)。研究方法是使用WEKA辅助工具进行实验,并对三种算法进行了比较测试。采用松茸菇科(Agaricus)和松茸菇科(Lepiota)的食用菌资料进行试验。蘑菇数据取自UCI机器学习存储库中的《奥杜邦学会北美蘑菇实地指南》。测试结果表明,C4.5算法与SVM准确率达到100%,但从速度上看,C4.5算法的处理速度要快于SVM。
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