Deep Learning with a Rethinking Structure for Multi-label Classification

Abstract

Multi-label classification (MLC) is an important class of machine learning problems that come with a wide spectrum of applications, each demanding a possibly different evaluation criterion. When solving the MLC problems, we generally expect the learning algorithm to take the hidden correlation of the labels into account to improve the prediction performance. Extracting the hidden correlation is generally a challenging task. In this work, we propose a novel deep learning framework to better extract the hidden correlation with the help of the memory structure within recurrent neural networks. The memory stores the temporary guesses on the labels and effectively allows the framework to rethink about the goodness and correlation of the guesses before making the final prediction. Furthermore, the rethinking process makes it easy to adapt to different evaluation criteria to match real-world application needs. In particular, the framework can be trained in an end-to-end style with respect to any given MLC evaluation criteria. The end-to-end design can be seamlessly combined with other deep learning techniques to conquer challenging MLC problems like image tagging. Experimental results across many real-world data sets justify that the rethinking framework indeed improves MLC performance across different evaluation criteria and leads to superior performance over state-of-the-art MLC algorithms.

References in corpus (7)

• Scikit-learn: Machine Learning in Python
• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• A Simple Way to Initialize Recurrent Networks of Rectified Linear Units
• CNN-RNN: A Unified Framework for Multi-label Image Classification
• Efficient Monte Carlo Methods for Multi-Dimensional Learning with Classifier Chains
• Consistent Multilabel Ranking through Univariate Losses