Plausible Explanations and Instance–Based Learning in Mixed Symbolic/Numeric Domains

The paper is concerned with supervised learning of numeric target concepts. The task is to learn to predict or determine the exact values of some numeric target variables. Training examples may be described by both symbolic and numeric predicates. General domain knowledge may be available in qualitative form. The paper presents a general learning model for such domains. The model integrates a symbolic learning component, which is based on a multi–instance plausible explanation algorithm, and an instance– based learning component, which stores instances with precise values and predicts new values by interpolation. The symbolic component can use available qualitative background knowledge; it learns sub–concepts that partition the space for the underlying instance–based method. A realization of the model in a system named IBL–Smart is then described. The system has been applied to a complex task from the domain of tonal music, and some experimental results are reported that demonstrate the effectiveness of the method.