Semi-supervised regression trees with application to QSAR modelling
Despite the ease of collecting abundance of data about various phenomena, obtaining
labeled data needed for learning models with high predictive performance remains a difficult …
labeled data needed for learning models with high predictive performance remains a difficult …
Feature ranking for semi-supervised learning
The data used for analysis are becoming increasingly complex along several directions:
high dimensionality, number of examples and availability of labels for the examples. This …
high dimensionality, number of examples and availability of labels for the examples. This …
Semi-supervised oblique predictive clustering trees
Semi-supervised learning combines supervised and unsupervised learning approaches to
learn predictive models from both labeled and unlabeled data. It is most appropriate for …
learn predictive models from both labeled and unlabeled data. It is most appropriate for …
Ensemble‐and distance‐based feature ranking for unsupervised learning
In this study, we propose two novel (groups of) methods for unsupervised feature ranking
and selection. The first group includes feature ranking scores (Genie3 score, RandomForest …
and selection. The first group includes feature ranking scores (Genie3 score, RandomForest …
Feature selection for analogy-based learning to rank
Learning to rank based on principles of analogical reasoning has recently been proposed
as a novel method in the realm of preference learning. Roughly speaking, the method …
as a novel method in the realm of preference learning. Roughly speaking, the method …
Ensemble-based feature ranking for semi-supervised classification
In this paper, we propose three feature ranking scores (Symbolic, Genie3, and Random
Forest) for the task of semi-supervised classification. In this task, there are only a few labeled …
Forest) for the task of semi-supervised classification. In this task, there are only a few labeled …