Evolving polydistributional mixtures for mammographic feature modeling and analysis

This work investigates the application of stochastic optimization in the development of a new paradigm, polydistributional mixtures, for automated data modeling. This new paradigm increases the generality of mixture methods by allowing for the automated simultaneous optimization of the number of components, the distributional form of each component, the proportionality associated with each component, as well as the parameters of each component. A general evolutionary approach, based on the mutation and selection from a population of potential solutions, is used as the optimization procedure. The approach is applied to modeling texture features extracted from digitized mammograms of cancerous and noncancerous regions. The resulting polydistributional mixtures are compared to optimal (in terms of model fitness) normal mixtures optimized via the expectation-maximization algorithm