Multiple sources/sensors/modalities Fusion: Registration of data from similar sources, approaches to data fusion from similar and heterogeneous data
*Feature selection and combination methods: * Feature extraction, dimensionality reduction
Randomized generation and selection of feature sets,
Feature selection and combination approaches
Classifier Ensemble Generation, Selection and Combination approaches:
Classifier diversity, diversity versus fusion accuracy
Homogeneous and heterogeneous classifier ensembles generation, selection and fusion, Parallel and serial fusion approaches, Optimal fusion rule selection: theoretical and empirical approaches Classifier selection: local context, and classifier competence regions
Role of data distributions in classification
Context-aware clustering for estimating distributions
Optimal classifier ensemble generation based on estimated data distribution