Multi-classification for high-dimensional data using probabilistic neural networks
Jingyi Li, Xiaojie Chao, Qin Xu · Journal of Radiation Research and Applied Sciences · 2022
Multi-classification tasks need sufficient information provided by the input data, whereas the input data lying in the high-dimensional space presents too sparse distributions to afford rich information, which creates trouble for multi-classification tasks. To address this, this paper proposed a probabilistic neural network approach. Firstly, the classification probability of high-dimensional data is calculated by using a novel Equation for Naïve Bayesian in order to reduce misclassification probability caused by the high dimension of the input data. Then, the calculated classification probability is fused into the cost function of the neural networks, forming the probabilistic neural networks. Results on high-dimensional real-world datasets and the National Surface Water Quality data provided by Environmental Monitoring Station, China show that the proposed method outperforms comparison methods in both classification ability and resisting the curse of dimensionality. We find that neural networks hardly obtain those advanced classification results upon a high-dimensional space because of the curse of dimensionality, but calculating classification probability of the input data can be confident to help neural networks to be resistance to the negative effects caused by high dimension of the input data.