Efficient learning ensemble SuperParent-one-dependence estimator by maximizing conditional log likelihood

作者：

Highlights：

• Existing shortages of main ensemble methodologies are studied.

• A novel ensemble SPODE algorithm by maximizing CLL is proposed.

• SGD iterative method is used to train the weight parameters.

• Analyses show EODE-CLL improves the performance of the entire ensemble classifiers.

• Experiments of EODE-CLL outperform significantly than state-of-the-art methods.

摘要

•Existing shortages of main ensemble methodologies are studied.•A novel ensemble SPODE algorithm by maximizing CLL is proposed.•SGD iterative method is used to train the weight parameters.•Analyses show EODE-CLL improves the performance of the entire ensemble classifiers.•Experiments of EODE-CLL outperform significantly than state-of-the-art methods.

论文关键词：Classification,Gradient methods,Machine learning,Modeling structured,Conditional likelihood

论文评审过程：Available online 28 May 2015, Version of Record 25 June 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.05.051