Fuzzy decision forest

We investigate the extension of fuzzy decision trees into fuzzy forests. Decision forests attempt to alleviate some problems often associated with decision trees: decision trees are minimalistic in their contained information, and they often degrade in complex domains, when multidimensional relationships are needed, or when there is no preference over similar actions. Moreover, the minimalistic approach often degrades the performance when some necessary features are either missing, noisy, or simply unreliable. These problems have been addressed in the last few years in hybrid systems, in which a number of distinct trees were extracted and used with some voting rules. Fuzzy decision forests follow the same ideas, except that they use more elaborate alternatives specific to local partitioning of the space. Therefore, it is an extension of those hybrid methods. In other words, while in those hybrid systems multiple choices are allowed at the level of the global search space, a fuzzy decision forest allows alternatives at every subspace level. Moreover the choice of available alternatives is data and domain-driven.