HIERARCHICAL ENSEMBLE LEARNING FOR MULTIMEDIA CATEGORIZATION AND AU T 0 A N N 0 TAT I 0 N
Serhiy Koisnov, ScBphane Marchand-hlaillet · 2004
T his paper presents a hierarchical ensemble learning method applied in the context of multimedia autoannotation. In contrast to the standard multiple-category classification setting that assumes independent, non-overlapping and exhaustive set of categories, the proposed approach models explicitly the hierarchi- cal relationships among target classes and estimates their relevance to a query as a trade-off between the goodness of fit to a given category description and its inherent uncertainty. The promising results of the empirical evaluation confirm the viability of the pro- posed approitrh, validated in comparison to several techniques of ensemble learning, as well as with different type of baseline classi- fiers.