Neural trees with peer-to-peer and server-to-client knowledge transferring models for high-dimensional data classification
作者：
Highlights：
• Clustering the features into some sets with highly related features and low innerredundancy.
• Defining a neural tree exploiting an ELM and an inference engine in any node.
• Transferring the rules extracted from any ELM to other nodes of neural tree.
• Proposing Peer-to-Peer (P2P) and Server-Client (SC) models for knowledge transferring.
• Classifying high dimensional data with high accuracy and without overfitting.
摘要
•Clustering the features into some sets with highly related features and low innerredundancy.•Defining a neural tree exploiting an ELM and an inference engine in any node.•Transferring the rules extracted from any ELM to other nodes of neural tree.•Proposing Peer-to-Peer (P2P) and Server-Client (SC) models for knowledge transferring.•Classifying high dimensional data with high accuracy and without overfitting.
论文关键词：Neural tree,Rule-base transferring,Feature clustering,Extreme learning machine,Communication models
论文评审过程：Received 11 August 2018, Revised 30 June 2019, Accepted 2 July 2019, Available online 2 July 2019, Version of Record 9 July 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.07.003
