55,750 research outputs found
A post-selection algorithm for improving dynamic ensemble selection methods
Dynamic Ensemble Selection (DES) is a Multiple Classifier Systems (MCS)
approach that aims to select an ensemble for each query sample during the
selection phase. Even with the proposal of several DES approaches, no
particular DES technique is the best choice for different problems. Thus, we
hypothesize that selecting the best DES approach per query instance can lead to
better accuracy. To evaluate this idea, we introduce the Post-Selection Dynamic
Ensemble Selection (PS-DES) approach, a post-selection scheme that evaluates
ensembles selected by several DES techniques using different metrics.
Experimental results show that using accuracy as a metric to select the
ensembles, PS-DES performs better than individual DES techniques. PS-DES source
code is available in a GitHub repositor
An Overview of Classifier Fusion Methods
A number of classifier fusion methods have been
recently developed opening an alternative approach
leading to a potential improvement in the
classification performance. As there is little theory of
information fusion itself, currently we are faced with
different methods designed for different problems and
producing different results. This paper gives an
overview of classifier fusion methods and attempts to
identify new trends that may dominate this area of
research in future. A taxonomy of fusion methods
trying to bring some order into the existing “pudding
of diversities” is also provided
An Overview of Classifier Fusion Methods
A number of classifier fusion methods have been
recently developed opening an alternative approach
leading to a potential improvement in the
classification performance. As there is little theory of
information fusion itself, currently we are faced with
different methods designed for different problems and
producing different results. This paper gives an
overview of classifier fusion methods and attempts to
identify new trends that may dominate this area of
research in future. A taxonomy of fusion methods
trying to bring some order into the existing “pudding
of diversities” is also provided
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