Fusion of dimensionality reduction methods: A case study in microarray classification

Dimensionality reduction has been demonstrated to improve the performance of the k-nearest neighbor (kNN) classifier for high-dimensional data sets, such as microarrays. However, the effectiveness of different dimensionality reduction methods varies, and it has been shown that no single method constantly outperforms the others. In contrast to using a single method, two approaches to fusing the result of applying dimensionality reduction methods are investigated: feature fusion and classifier fusion. It is shown that by fusing the output of multiple dimensionality reduction techniques, either by fusing the reduced features or by fusing the output of the resulting classifiers, both higher accuracy and higher robustness towards the choice of number of dimensions is obtained.