Computers, Volume 13, Issue 1
January 2024 - 31 articles
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Cover Story: Automatic data fusion is an important field of machine learning that has been increasingly studied. The objective is to improve the classification performance from several individual classifiers in terms of result accuracy and stability. The fusion step can be applied at early and/or late stages of the classification procedure. Early fusion consists of combining features from different sources or domains before training the individual classifiers. In turn, late fusion combines the results from the individual classifiers after testing. Late fusion has two setups, combination of posterior probabilities (soft fusion) and combination of decisions (hard fusion). A theoretical analysis of the conditions for applying early and late fusion and a study on recent data fusion methods are provided. View this paper