This study explains why prior works relied on this problematic function without considering well-developed probabilistic outputs for binary linear classification and leads to a more suitable probability estimation for tree-based linear methods.
This study innovatively analyzes the hyperparameters of tree-based linear methods and suggests an efficient and effective guideline that leads to consistent improvements across datasets, thereby strengthening tree-based linear methods as a stronger XMTC baseline.
Evaluating the performance of Greedy K-center across a variety of metric spaces shows that mapping unlabeled instances into a predictive probability space and weighting the result by entropy often dominates the other options for active learning selection with Greedy K-center.
This paper describes formally how MLC can be transformed into an order structure learning and prediction task, and then proceeds to study the problem of predicting Bayes-optimal order structures.
The adaptive classification-risk design attains lower mean error than this Fisher comparator across the labeling budgets considered, although it does not uniformly outperform entropy or margin sampling and differences among the targeted strategies become small as the labeling budget increases.
F. Setoudehtanzangi, Geoffrey J. McLachlan· 0 citations
Data-Informed Centroid Splitting (DICS), a clustering-based framework that constructs a compact and informative set of candidate splits using data-driven priors, significantly reduces the split search space for classification tasks while preserving predictive performance.
This work studies a different regime in which the probability of label missingness depends on posterior classification uncertainty, so that the observed missing-label indicators can themselves carry information about the Bayes decision boundary.
Huanchao Zhou, Jin-Ran Wu, Fariborz Setoudehtazang et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
