An improved multi-label classification based on label ranking and delicate boundary SVM

• Published in: The 2010 International Joint Conference on Neural Networks (IJCNN) pp. 1 - 6
• Main Authors: Benhui Chen, Weifeng Gu, Jinglu Hu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2010
• Subjects: Accuracy; Benchmark testing; Probabilistic logic; Support vector machines; Training; Training data
• ISBN: 9781424469161; 1424469163
• ISSN: 2161-4393; 2161-4407
• DOI: 10.1109/IJCNN.2010.5596350

In this paper, an improved multi-label classification is proposed based on label ranking and delicate decision boundary SVM. Firstly, an improved probabilistic SVM with delicate decision boundary is used as the scoring method to obtain a proper label rank. It can improve the probabilistic label rank by introducing the information of overlapped training samples into learning procedure. Secondly, a threshold selection related with input instance and label rank is proposed to decide the classification results. It can estimate an appropriate threshold for each testing instance according to the characteristics of instance and label rank. Experimental results on four multi-label benchmark datasets show that the proposed method improves the performance of classification efficiently, compared with binary SVM method and some existing well-known methods.