An adaptive rule-based classifier for mining big biological data
作者：
Highlights：
• The adaptive rule-based classifier is used to classify multi-class biological data.
• It applies random subspace and boosting approaches with ensemble of decision trees.
• Decision tree induction is used for evolving rules from the biological data.
• k-nearest-neighbor is used for removing ambiguity between the contradictory rules.
• The classifier is evaluated using 148 Exome data sets and 10 life sciences data sets.
摘要
•The adaptive rule-based classifier is used to classify multi-class biological data.•It applies random subspace and boosting approaches with ensemble of decision trees.•Decision tree induction is used for evolving rules from the biological data.•k-nearest-neighbor is used for removing ambiguity between the contradictory rules.•The classifier is evaluated using 148 Exome data sets and 10 life sciences data sets.
论文关键词：Brugada syndrome,Classification,Decision tree,Genomic data,Rule-based classifier
论文评审过程：Received 2 January 2016, Revised 1 August 2016, Accepted 2 August 2016, Available online 3 August 2016, Version of Record 4 August 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.08.008
