0000000000286100
AUTHOR
Yoisel Campos
A local complexity based combination method for decision forests trained with high-dimensional data
Accurate machine learning with high-dimensional data is affected by phenomena known as the “curse” of dimensionality. One of the main strategies explored in the last decade to deal with this problem is the use of multi-classifier systems. Several of such approaches are inspired by the Random Subspace Method for the construction of decision forests. Furthermore, other studies rely on estimations of the individual classifiers' competence, to enhance the combination in the multi-classifier and improve the accuracy. We propose a competence estimate which is based on local complexity measurements, to perform a weighted average combination of the decision forest. Experimental results show how thi…
A Feature Set Decomposition Method for the Construction of Multi-classifier Systems Trained with High-Dimensional Data
Data mining for the discovery of novel, useful patterns, encounters obstacles when dealing with high-dimensional datasets, which have been documented as the "curse" of dimensionality. A strategy to deal with this issue is the decomposition of the input feature set to build a multi-classifier system. Standalone decomposition methods are rare and generally based on random selection. We propose a decomposition method which uses information theory tools to arrange input features into uncorrelated and relevant subsets. Experimental results show how this approach significantly outperforms three baseline decomposition methods, in terms of classification accuracy.