Classifier ensemble creation via false labelling

2015/07/17 by Bálint Antal · 3 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial intelligence #Cascading classifiers #Classifier (UML) #Computer science #Ensemble learning #Gene expression and cancer classification #Labelling #Machine Learning and Data Classification #Machine learning #Neural Networks and Applications #Pattern recognition (psychology) #Preprocessor #Random subspace method #cs.LG

paper · pdf · doi:10.1016/j.knosys.2015.07.009

published in Knowledge-Based Systems 89, 278-287 (Elsevier BV)

openalex publication_date 2015/07/17 · arxiv created 2016/03/05 · arxiv updated 2016/03/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

In this paper, a novel approach to classifier ensemble creation is presented. While other ensemble creation techniques are based on careful selection of existing classifiers or preprocessing of the data, the presented approach automatically creates an optimal labelling for a number of classifiers, which are then assigned to the original data instances and fed to classifiers. The approach has been evaluated on high-dimensional biomedical datasets. The results show that the approach outperformed individual approaches in all cases.

Citations

• A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
• An ensemble-based system for automatic screening of diabetic retinopathy
• Comparing Individual Means in the Analysis of Variance
• Bagging predictors
• The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance
• A desicion-theoretic generalization of on-line learning and an application to boosting