Ensemble classification in steganalysis – Cross-validation and AdaBoost
نویسنده
چکیده
Two alternative designs to the ensemble classifier proposed in [13] are studied in this report. First, the out-of-bag error estimation is replaced with crossvalidation. Second, we incorporate AdaBoost and modify the weights of the individual training samples as the training progresses. The final decision is formed as a weighted combination of individual predictions rather than through majority voting. We experimentally compare both alternatives with the original design and conclude that they bring no performance gain.
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