Local Learning for Multi-layer, Multi-component Predictive System
Authors: Al-Jubouri, B. and Gabrys, B.
Journal: Procedia Computer Science
Volume: 96
Pages: 723-732
eISSN: 1877-0509
DOI: 10.1016/j.procs.2016.08.256
Abstract:This study introduces a new multi-layer multi-component ensemble. The components of this ensemble are trained locally on subsets of features for disjoint sets of data. The data instances are assigned to local regions using the similarity of their features pairwise squared correlation. Many ensemble methods encourage diversity among their base predictors by training them on different subsets of data or different subsets of features. In the proposed architecture the local regions contain disjoint sets of data and for this data only the most similar features are selected. The pairwise squared correlations of the features are used to weight the predictions of the ensemble's models. The proposed architecture has been tested on a number of data sets and its performance was compared to five benchmark algorithms. The results showed that the testing accuracy of the developed architecture is comparable to the rotation forest and is better than the other benchmark algorithms.
https://eprints.bournemouth.ac.uk/24675/
Source: Scopus
Local learning for multi-layer, multi-component predictive system
Authors: Al-Jubouri, B. and Gabrys, B.
Journal: KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS: PROCEEDINGS OF THE 20TH INTERNATIONAL CONFERENCE KES-2016
Volume: 96
Pages: 723-732
ISSN: 1877-0509
DOI: 10.1016/j.procs.2016.08.256
https://eprints.bournemouth.ac.uk/24675/
Source: Web of Science (Lite)
Local learning for multi-layer, multi-component predictive system
Authors: Al-Jubouri, B. and Gabrys, B.
Journal: Procedia Computer Science
Publisher: Elsevier: Creative Commons Attribution Non-Commercial No-Derivatives License
ISSN: 1877-0509
Abstract:This study introduces a new multi-layer multi-component ensemble. The components of this ensemble are trained locally on subsets of features for disjoint sets of data. The data instances are assigned to local regions using the similarity of their features pairwise squared correlation. Many ensemble methods encourage diversity among their base predictors by training them on different subsets of data or different subsets of features. In the proposed architecture the local regions contain disjoint sets of data and for this data only the most similar features are selected. The pairwise squared correlations of the features are used to weight the predictions of the ensemble's models. The proposed architecture has been tested on a number of data sets and its performance was compared to five benchmark algorithms. The results showed that the testing accuracy of the developed architecture is comparable to the rotation forest and is better than the other benchmark algorithms.
https://eprints.bournemouth.ac.uk/24675/
Source: Manual
Local Learning for Multi-layer, Multi-component Predictive System.
Authors: Al-Jubouri, B. and Gabrys, B.
Editors: Howlett, R.J., Jain, L.C., Toro, C. and Lim, C.P.
Journal: KES
Volume: 96
Pages: 723-732
Publisher: Elsevier
https://eprints.bournemouth.ac.uk/24675/
http://www.sciencedirect.com/science/journal/18770509/96
Source: DBLP
Local learning for multi-layer, multi-component predictive system.
Authors: Al-Jubouri, B. and Gabrys, B.
Journal: Procedia Computer Science
Volume: 96
Pages: 723-732
ISSN: 1877-0509
Abstract:This study introduces a new multi-layer multi-component ensemble. The components of this ensemble are trained locally on subsets of features for disjoint sets of data. The data instances are assigned to local regions using the similarity of their features pairwise squared correlation. Many ensemble methods encourage diversity among their base predictors by training them on different subsets of data or different subsets of features. In the proposed architecture the local regions contain disjoint sets of data and for this data only the most similar features are selected. The pairwise squared correlations of the features are used to weight the predictions of the ensemble's models. The proposed architecture has been tested on a number of data sets and its performance was compared to five benchmark algorithms. The results showed that the testing accuracy of the developed architecture is comparable to the rotation forest and is better than the other benchmark algorithms.
https://eprints.bournemouth.ac.uk/24675/
Source: BURO EPrints