A new ensemble method with feature space partitioning for high-dimensional data classification

DOI10.1155/2015/590678zbMath1395.62171OpenAlexW2111575929WikidataQ59119490 ScholiaQ59119490MaRDI QIDQ1666085

Cheng Hao Jin, Buhyun Hwang, Minghao Piao, Ji-Moon Chung, Ho Sun Shon, Yongjun Piao, Keun Ho Ryu

Publication date: 27 August 2018

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2015/590678

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Uses Software

• CMU PIE
• AR face

Cites Work

• Bagging predictors
• Robust twin support vector machine for pattern classification
• Feature subset selection in large dimensionality domains
• Classification by ensembles from random partitions of high-dimensional data
• Attribute bagging: Improving accuracy of classifier ensembles by using random feature subsets.
• A novel representation for apoptosis protein subcellular localization prediction using support vector machine
• Cancer classification using ensemble of neural networks with multiple significant gene subsets
• Genetic algorithm-based feature set partitioning for classification problems
• Brain Reading Using Full Brain Support Vector Machines for Object Recognition: There Is No “Face” Identification Area
• Random forests

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