Efficient monte carlo methods for multi-dimensional learning with classifier chains

作者：

Highlights：

• A Monte Carlo approach for efficient classifier chains.

• Applied to learning from multi-label and multi-dimensional data.

• A theoretical and empirical study of payoff functions in the search space.

• An empirical cross-fold comparison with PCC and other related methods.

摘要

Highlights•A Monte Carlo approach for efficient classifier chains.•Applied to learning from multi-label and multi-dimensional data.•A theoretical and empirical study of payoff functions in the search space.•An empirical cross-fold comparison with PCC and other related methods.

论文关键词：Classifier chains,Multi-dimensional classification,Multi-label classification,Monte Carlo methods,Bayesian inference

论文评审过程：Received 1 April 2013, Revised 30 September 2013, Accepted 3 October 2013, Available online 14 October 2013.

论文官网地址：https://doi.org/10.1016/j.patcog.2013.10.006