Abstract
In previous chapters, we have introduced the Linguistic Decision Tree model and shown how this model can be used for classification and prediction. However, for some complex problems, good probability estimations can only be obtained by deep LDTs, which have low transparency. In such cases, how can we build a model which has a good probability estimation but which uses compact LDTs? In this chapter, two hybrid learning models are proposed combining the LDT model and the fuzzy Naive Bayes classifier. In the first model, an unlabeled instance is classified according to the Bayesian estimation given a single LDT. In the second model, a set of disjoint LDTs are used as Bayesian estimators. Experimental studies show that the first new hybrid models has both better accuracy and transparency when compared to fuzzy Naive Bayes and LDTs at shallow tree depths. The second model is shown to have equivalent performance to the LDT model.
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