.png){kind=icon}
返回
Modified spider monkey optimization algorithm based feature selection and probabilistic neural network classifier in face recognition
.png){kind=icon}
PRE
AI
DOI：10.1111/exsy.13088.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper proposes a novel and robust predictive method using modified spider monkey optimization (MSMO) and probabilistic neural network (PNN) for face recognition. The limitation of the traditional spider monkey optimization (SMO) approach to obtaining an optimal solution for classification problems is overcome by enhancing the performance of SMO by modifying the perturbation rate with a non-linear function, thereby improving the convergence of SMO. The framework comprises image preprocessing, feature extraction using dual tree complex wavelet transform (DT-CWT), feature selection using the modified spider monkey optimization algorithm (MSMO), and classification using PNN. The proposed method is tested on the Yale and AR Face datasets. Experimental outcomes reveal that the proposed framework attain an accuracy of 99.4% with appreciable sensitivity, specificity, and G-mean. To examine the efficacy of MSMO, parametric studies are conducted, which showed that MSMO converges faster with high fitness when compared to similar evolutionary algorithms like Genetic Algorithm (GA), Grey Wolf Optimization Algorithm (GWO), Particle Swarm Algorithm (PSO), and Cuckoo Search (CS) in selecting the optimal feature set. The MSMO-PNN method outperforms similar state-of-the-art methods, which reveals that the method proposed is competitive. The proposed model is robust to Gaussian and salt-pepper noise, obtaining the highest accuracy of 97.89% for varied noise density and variance.
Keyword:
classification
DTCWT
probabilistic neural network
spider monkey optimization
swarm intelligence
期刊
IF:
2.3
论文数:
2.6K
被引数:
3.8K
机构
暂无机构信息
引用论文
.png){kind=icon}
INFO: An efficient optimization algorithm based on weighted mean of vectorsINFO: 一种基于向量加权均值的高效优化算法
PREAI
分享收藏
查看更多内容