Edwin Lughofer, Stefan Kindermann,
"Rule Weight Optimization and Feature Selection in Fuzzy Systems with Sparsity Constraints"
: Proceedings of the IFSA/EUSFLAT 2009 conference, Nummer 950-956, 2009
Rule Weight Optimization and Feature Selection in Fuzzy Systems with Sparsity Constraints
Sprache des Titels:
Proceedings of the IFSA/EUSFLAT 2009 conference
In this paper, we are dealing with a novel datadriven
learning method (SparseFIS) for Takagi-Sugeno fuzzy
systems, extended by including rule weights. Our learning
method consists of three phases: the first phase conducts a
clustering process in the input/output feature space with iterative vector quantization. Hereby, the number of clusters = rules is pre-defined and denotes a kind of upper bound on a reasonable granularity. The second phase optimize the rule weights in the fuzzy systems with respect to least squares error measure by applying a sparsity-constrained steepest descent optimization procedure. This is done in a coherent optimization
procedure together with elicitation of consequent parameters.
Depending on the sparsity threshold, more or less rules
weights can be forced towards 0, switching off some rules (rule
selection). The third phase estimates the linear consequent parameters
by a regularized sparsity constrained optimization
procedure for each rule separately (local learning approach).
Sparsity constraints are applied here in order to force linear parameters
to be 0, triggering a feature selection mechanism per
rule. The method is evaluated based on high-dimensional data
from industrial processes and based on benchmark data sets
from the internet and compared to well-known batch training
methods in terms of accuracy and complexity of the fuzzy systems.