计算机科学
水准点(测量)
人工智能
机器学习
光学(聚焦)
树(集合论)
深度学习
回归
集合(抽象数据类型)
特征(语言学)
集成学习
人工神经网络
代表(政治)
数据挖掘
模式识别(心理学)
数学
统计
地理
法学
程序设计语言
物理
哲学
数学分析
大地测量学
光学
政治
语言学
政治学
作者
Fábio Nakano,Κωνσταντίνος Πλιάκος,Celine Vens
标识
DOI:10.1016/j.patcog.2021.108211
摘要
Recently, deep neural networks have expanded the state-of-art in various scientific fields and provided solutions to long standing problems across multiple application domains. Nevertheless, they also suffer from weaknesses since their optimal performance depends on massive amounts of training data and the tuning of an extended number of parameters. As a countermeasure, some deep-forest methods have been recently proposed, as efficient and low-scale solutions. Despite that, these approaches simply employ label classification probabilities as induced features and primarily focus on traditional classification and regression tasks, leaving multi-output prediction under-explored. Moreover, recent work has demonstrated that tree-embeddings are highly representative, especially in structured output prediction. In this direction, we propose a novel deep tree-ensemble (DTE) model, where every layer enriches the original feature set with a representation learning component based on tree-embeddings. In this paper, we specifically focus on two structured output prediction tasks, namely multi-label classification and multi-target regression. We conducted experiments using multiple benchmark datasets and the obtained results confirm that our method provides superior results to state-of-the-art methods in both tasks.
科研通智能强力驱动
Strongly Powered by AbleSci AI