Original scientific paper

https://doi.org/10.31534/engmod.2018.4.si.07s

Multi-Label Classification Based on the Improved Probabilistic Neural Network

Huilong Fan ; Guizhou Key Laboratory of Public Big Data, Guizhou University, Guiyang, CHINA
Yongbin Qin ; College of Computer Science and Technology, Guizhou University, Guiyang, CHINA

Abstract

This paper aims to overcome the defects of the existing multi-label classification methods, such as the insufficient use of label correlation and class information. For this purpose, an improved probabilistic neural network for multi-label classification (ML-IPNN) was developed through the following steps. Firstly, the traditional PNN was structurally improved to fit in with multi-label data. Then secondly, a weight matrix was introduced to represent the label correlation and synthetize the information between classes, and the ML-IPNN was trained with the backpropagation mechanism. Finally, the classification results of the ML-IPNN on three common datasets were compared with those of the seven most popular multi-label classification algorithms. The results show that the ML-IPNN outperformed all contrastive algorithms. The research findings brought new light on multi-label classification and the application of artificial neural networks (ANNs).

Keywords

multi-label classification; probabilistic neural network (PNN); classification; label correlation.

Hrčak ID:

218255

URI

https://hrcak.srce.hr/218255

Publication date:

27.3.2019.

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