A novel ECOC algorithm for multiclass microarray data classification based on data complexity analysis
作者：
Highlights：
• We proposed a novel ECOC algorithm for multiclass microarray data classification based on the data complexity theory.
• Various data complexity measures are deployed to detect the intrinsic characteristics of microarray data sets, so as to produce diverse coding matrices.
• A new data complexity measure, named as C1, is designed to evaluate data distribution. It benefits the optimization process of our class partition.
• The proposed ECOC algorithm performs more stably in most multiclass microarray data sets compared with other popular ECOC algorithms.
摘要
•We proposed a novel ECOC algorithm for multiclass microarray data classification based on the data complexity theory.•Various data complexity measures are deployed to detect the intrinsic characteristics of microarray data sets, so as to produce diverse coding matrices.•A new data complexity measure, named as C1, is designed to evaluate data distribution. It benefits the optimization process of our class partition.•The proposed ECOC algorithm performs more stably in most multiclass microarray data sets compared with other popular ECOC algorithms.
论文关键词：Error correcting output codes (ECOC),Data complexity,Microarray data,Multiclass
论文评审过程：Received 20 February 2018, Revised 18 September 2018, Accepted 17 January 2019, Available online 4 February 2019, Version of Record 8 February 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.01.047
