Hybrid and ensemble techniques in soft computing: recent advances and emerging trends

The application of hybrid and ensemble methodologies in the field of soft computing (SC) and machine learning (ML) has become more visible and attractive. The relevance of these methodologies is motivated by their power of being able to express knowledge contained in data sets in multiple ways, benefiting each of the other, i.e., exploiting their diversity, thus increasing the performance of sole base models in terms of model accuracy and generalization capability by intelligent combination strategies, especially while dealing with high-dimensional complex regression and classification problems. Another main reason for their popularity is the high complementary of its components. The integration of the basic technologies into hybrid machine learning solutions facilitates more intelligent search, enhanced optimization, reasoning and hybridization methods that match various domain knowledge with empirical data to solve advanced and complex problems.