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Boosting Classifiers Built from Different Subsets of Features

Janodet J-C., Sebban M., Suchier H-M.

Fundamenta Informaticae

2009

Vol. 96, nr 1/2

89-109

We focus on the adaptation of boosting to representation spaces composed of different subsets of features. Rather than imposing a single weak learner to handle data that could come from different sources (e.g., images and texts and sounds), we suggest the decomposition of the learning task into several dependent sub-problems of boosting, treated by different weak learners, that will optimally collaborate during the weight update stage. To achieve this task, we introduce a new weighting scheme for which we provide theoretical results. Experiments are carried out and show that ourmethod works significantly better than any combination of independent boosting procedures.

Supervised pattern recognition with heterogeneous features

Valev V.

Machine Graphics and Vision

2004

Vol. 13, No. 4

345-353

In this paper, we address the supervised pattern recognition problem with heterogeneous features, where the mathematical model is based on construction of thresholds. Non-Reducible Descriptors (NRDs) for fuzzy features are obtained through the use of a threshold value, which is calculated based on the distance between patterns. In case of solving the problem with real features, the mathematical model for construction of thresholds is based on parallel feature partitioning. Boolean formulas are used to represent NRDs.