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Dataset analysis for classifier ensemble enhancement

We developed three different methods for dataset analysis and ensemble enhance- 
ment. They share the underlying idea that an accurate preprocessing and adap- 
tation of the data can improve the system performance, without changing the 
classification model. Correlation Score is a generic framework for assessing encoding 
techniques by measuring the correlation between the encoded feature vectors and 
the corresponding class labels; experiments show its effectiveness in discovering the 
best encoding configurations between those tested, on a wide range of classification 
domains. Multi-Resolution Complexity Analysis is a method for assessing the 
local complexity inside a given domain. It is able to split a domain into regions 
of different classification complexity, giving insights on the inner structure of the 
populations inside the domain. Finally, Forests of Local Trees are a novel training 
algorithm for ensemble classifiers. They are based on the concept of local trees: 
classifiers trained with a bias toward a certain region of the domain. This bias 
enhances the diversity inside the ensemble, leading to improved performance. 
These three topics are meant as a foundation for a more complex framework, that 
will eventually utilize them organically.