Balancing Interpretability and Accuracy by Multi-Level Fuzzy Information Granulation

In this paper we present a multi-level approach for extracting well-defined and semantic ally sound information granules from numerical data. The approach is based on the Double Clustering framework (DCf), which performs two main clustering steps on the data space in order to extract granules qualitatively described in terms of fuzzy sets that meet a number of interpretability constraints. While DCf can extract information granules with a fixed level of granulation, its multi-level extension, called ML-DC (Multi-Level Double Clustering), can perform granulation of data at different levels, in a hierarchical fashion. At the first level, the whole dataset is granulated. At the second level, data embraced in each first-level granule are further granulated taking into account the context generated by that granule. The hierarchical collection of granules derived via ML-DC is then used to construct a committee of fuzzy inference systems that can approximate any I/O mapping with a good balance between accuracy and interpretability.