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http://dx.doi.org/10.6109/jkiice.2020.24.2.225

A Reconstruction of Classification for Iris Species Using Euclidean Distance Based on a Machine Learning  

Nam, Soo-Tai (Institute of General Education, Pusan National University)
Shin, Seong-Yoon (School of Computer Information & Communication Engineering, Kunsan National University)
Jin, Chan-Yong (Division of Information & Electronic Commerce, Wonkwang University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.2, 2020 , pp. 225-230 More about this Journal
Abstract
Machine learning is an algorithm which learns a computer based on the data so that the computer can identify the trend of the data and predict the output of new input data. Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is a way of learning a machine with given label of data. In other words, a method of inferring a function of the system through a pair of data and a label is used to predict a result using a function inferred about new input data. If the predicted value is continuous, regression analysis is used. If the predicted value is discrete, it is used as a classification. A result of analysis, no. 8 (5, 3.4, setosa), 27 (5, 3.4, setosa), 41 (5, 3.5, setosa), 44 (5, 3.5, setosa) and 40 (5.1, 3.4, setosa) in Table 3 were classified as the most similar Iris flower. Therefore, theoretical practical are suggested.
Keywords
Data mining; Machine learning; Classification; Supervised learning; Regression analysis;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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