Predictive weighting for cluster ensembles
Smyth, Christine, and Coomans, Danny (2007) Predictive weighting for cluster ensembles. Journal of Chemometrics, 21 (7-9). pp. 364-375.
|PDF - Repository staff only - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader|
View at Publisher Website: http://dx.doi.org/10.1002/cem.1048
An ensemble of regression models predicts by taking a weighted average of the predictions made by individual models. Calculating the weights such that they reflect the accuracy of individual models (post processing the ensemble) has been shown to increase the ensemble's accuracy. However, post processing cluster ensembles has not received as much attention because of the inherent difficulty in assessing the accuracy of an individual cluster model. By enforcing the notion that clusters must be predictable, this paper suggests a means of implicitly post processing cluster ensembles by drawing analogies with regression post processing techniques. The product of the post processing procedure is an intelligently weighted co-occurrence matrix. A new technique, similarity-based k-means (SBK), is developed to split this matrix into clusters. The results using three real life datasets underpinned by chemical and biological phenomena show that splitting an intelligently weighted co-occurrence matrix gives accuracy that approaches supervised classification methods.
|Item Type:||Article (Refereed Research - C1)|
|Keywords:||post processing; cluster ensembles|
|FoR Codes:||01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 50%|
03 CHEMICAL SCIENCES > 0301 Analytical Chemistry > 030106 Quality Assurance, Chemometrics, Traceability and Metrological Chemistry @ 50%
|SEO Codes:||86 MANUFACTURING > 8608 Human Pharmaceutical Products > 860803 Human Pharmaceutical Treatments (e.g. Antibiotics) @ 100%|
|Deposited On:||07 May 2009 11:19|
|Last Modified:||18 Oct 2013 00:28|
Last 12 Months: 0
|Citation Counts with External Providers:|
Repository Staff Only: item control page