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This paper deals with the problem of missing values in decision trees during classification. Our approach is derived from the ordered attribute trees method, proposed by Lobo and Numao in 2000, which builds a decision tree for each attribute and uses these trees to fill the missing attribute values. Our method takes into account the dependence between attributes by using Mutual Information. The result of the classification process is a probability distribution instead of a single class. In this paper, we present tests performed on several databases using our approach and Quinlan's method. We also measure the quality of our classification results. Finally, we discuss some perspectives.

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This paper concerns a home automation system of energy management. Such a system aims at keeping under control the energy consumption in housing. The expected energy consumption is scheduled over one day. Each hour a total amount of energy is available that is a resource constraint for the expected energy plan. The expected consumption is totally derived from users behavior which are quite different from one housing to another, and rather difficult to predict. This paper proposes a Learning System to predict the user's requests of energy. The proposed method relies on Bayesian networks.