Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization

Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization

Fruit classification is quite difficult because of the various categories and similar shapes and features of fruit. In this work, we proposed two novel machine-learning based classification methods. The developed system consists of wavelet entropy (WE), principal component analysis (PCA), feedforwar...

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Bibliographic Details
Main Authors: Shuihua Wang, Yudong Zhang, Genlin Ji, Jiquan Yang, Jianguo Wu, Ling Wei
Format: Article
Language:English
Published: MDPI AG 2015-08-01
Series:Entropy
Subjects:
Shannon entropy
machine learning
fruit classification
wavelet transform
feed-forward neural network
artificial bee colony
biogeography-based optimization
Online Access:http://www.mdpi.com/1099-4300/17/8/5711

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