Rank thresholds in classifier ensembles in medical diagnosis
Journal Title: Computer Science and Mathematical Modelling - Year 2016, Vol 0, Issue 3
Abstract
Classification methods have multiple applications, with medical diagnosis being one of the most common. A powerful way to improve classification quality is to combine single classifiers into an ensemble. One of the approaches for creating such ensembles is to combine class rankings from base classifiers. In this paper, two rank-based ensemble methods are studied: Highest Rank and Borda Count. Furthermore, the effect of applying class rank threshold to these methods is analyzed. We performed tests using real-life medical data. It turns out that specificity of data domain can affect classification quality depending on classifier type.
Authors and Affiliations
Karol Antczak
Keywords
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- EP ID EP186316
- DOI 10.5604/01.3001.0009.4412
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