Application of Naive Bayesian Classifier Based on Gaussian Distribution in Plant Recognition

被引：1
|
作者：
Zhu, Liqiang [1 ]
机构：
[1] China West Normal Univ, Coll Comp, Nanchong, Sichuan, Peoples R China
来源：
PROCEEDINGS OF 2021 IEEE 12TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS) | 2021年
关键词：
component; naive Bayesian classifier; plant classification; iris; Gaussian distribution; distribution function;
D O I：
10.1109/ICSESS52187.2021.9522331
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
This paper presented a kind of plant leaf classification method based on naive Bayesian classifier and leaf shape features. Firstly, estimate the class-conditional probability of the numeric features with the distribution function of Gaussian distribution, then reduce the deviation of probability statistics of the nominal features with Laplace smoothing. In the end, compare and analyse the influence of equal-width binning, equal-frequency binning, MDL discretization method and Gaussian probability estimation method on the classification efficiency. The experimental results on iris data set indicated that the correct classification rate of this method reached 95.3%, and the method was feasible and effective.
引用
收藏
页码：135 / 139
页数：5
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