Hierarchical multi-label classification using local neural networks
作者：
Highlights：
• We incrementally train a multi-layer perceptron for each level of the classification hierarchy.
• Predictions made in a level are used as inputs to the neural network associated to the next level.
• Local information learned in a level was useful to learn a neural network in the next level.
• We discussed about the smaller number of predictions made by our method in the deeper hierarchical levels.
• Experimental analysis show that our method obtains better or competitive results.
摘要
•We incrementally train a multi-layer perceptron for each level of the classification hierarchy.•Predictions made in a level are used as inputs to the neural network associated to the next level.•Local information learned in a level was useful to learn a neural network in the next level.•We discussed about the smaller number of predictions made by our method in the deeper hierarchical levels.•Experimental analysis show that our method obtains better or competitive results.
论文关键词：Hierarchical multi-label classification,Neural networks,Local classification method
论文评审过程：Received 16 July 2012, Revised 25 November 2012, Accepted 14 March 2013, Available online 22 March 2013.
论文官网地址：https://doi.org/10.1016/j.jcss.2013.03.007
