A feature regulation application method for hierarchical decision learning systems receives a feature regulation training data. A feature regulation method uses the feature regulation training data and invokes a plurality of the hierarchical decision learning to create feature subset information output. The feature regulation application method also receives a learning data. A feature sampling method uses the feature subset information and the learning data to create a feature subset learning data output. A hierarchical decision learning method uses the feature subset learning data to create a hierarchical decision system output. The feature regulation method also outputs feature ranking information. A feature regulated hierarchical decision learning method uses the feature subset learning data and the feature ranking information to create a hierarchical decision system output. This invention performs feature selection using a feature regulation method designed specifically for hierarchical decision learning systems such as decision tree classifiers. It provide a computationally feasible method for feature selection that considers the hierarchical decision learning systems used for decision making. It evaluates the stability of features subject to context switching and the reliability of the tree nodes by information integration. It provides the ranking of the features that can be incorporated in the creation of the hierarchical decision learning systems.
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