Introduction to the concept of structural HMM: application to mining customers´ preferences in automotive design

We have introduced in this paper the concept of structural hidden Markov models (SHMM). This new paradigm adds the syntactical (or structural) component to the traditional HMM. SHMM introduce relationships between the visible observations of a sequence. These observations are related because they are viewed as evidences of a same conclusion in a rule of inference. We have applied this novel concept to predict customer´s preferences for automotive designs. SHMM has outperformed both the k-nearest neighbors and the neural network classifiers with an additional 12% increase in accuracy.