An improved multi-instance multi-label learning algorithm based on representative instances selection and label correlations

作者：Liu Chanjuan^*; Chen Tongtong; Zou Hailin; Ding Xinmiao; Wang Yuling

来源：International Journal of Grid and Utility Computing, 2018, 9(3): 268-277.

DOI：10.1504/IJGUC.2018.093955

• Audio data
• Instance selection
• Label correlations
• Multi-instance multi-label learning
• Optimisations
• Representative instances selection
• State-of-the-art methods
• Text classification

Multi-Instance Multi-Label Learning (MIML) has been successfully used in image and text classification problems. It is noteworthy that few of the previous studies consider the patternlabel relations. Inevitably, there are some useless instances in a bag which will reduce the accuracy of the annotation. In this paper we focus on this problem. Firstly, an instance selection method via joint l2,1-norms constraint is employed to eliminate the useless instances and select the representative instances by modelling the instance correlation. Then, bags are mapped to these representative instances. Finally, the classifier is trained by an optimisation algorithm based on label correlations. Experimental results on image data set, text data sets and bird song audio data set show that the proposed algorithm significantly improves the performance of MIML classifier compared with the state-of-the-art methods.

• 出版日期2018