Abstract
Numerous machine learning problems involve an investigation of relationships between features in heterogeneous datasets, where different classifier can be more appropriate for different regions. We propose a technique of localized cascade generalization of weak classifiers. This technique identifies local regions having similar characteristics and then uses the cascade generalization of local experts to describe the relationship between the data characteristics and the target class. We performed a comparison with other well known combining methods using weak classifiers as base-learners, on standard benchmark datasets and the proposed technique was more accurate.