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This paper presents the Feature Decomposition Approach for improving supervised learning tasks. While in Feature Selection the aim is to identify a representative set of features from which to construct a classification model, in Feature Decomposition, the goal is to decompose the original set of features into several subsets. A classification model is built for each subset, and then all generated models are combined. This paper presents theoretical and practical aspects of the Feature Decomposition Approach. A greedy procedure, called DOT (Decomposed Oblivious Trees), is developed to decompose the input features set into subsets and to build a classification model for each subset separately. The results achieved in the empirical comparison testing with well-known learning algorithms (like C4.5) indicate the superiority of the feature decomposition approach in learning tasks that contains high number of features and moderate numbers of tuples.