Boosting-based Sequential Output Prediction
Sequence prediction problem has been traditionally identified in the literature with sequence labeling. This approach typically corresponds to the classification of a label sequence associated to observed input sequence. However, another interpretation of sequence prediction may be considered where a label sequence (sequential output) is classified based only on the independent set of attributes. The paper presents a new, based on boosting, ensemble approach, performing such sequential output; prediction. The sequential nature of the classified structure is reflected on the applied cost function. The experimental results reported in the paper revealed a high validity and competitiveness of the proposed approach.
sequential output prediction sequence prediction classification adaboostseq structured output learning structured output prediction structured prediction multi-label classification ensemble method classifier fusion