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سند علمی

Label Ranking with Partial Abstention using Ensemble Learning

Weiwei Cheng, Eyke Hüllermeier

چکیده

In label ranking, the problem is to learn a mapping from instances to rankings over a finite set of predefined class labels. In this paper, we consider a generalization of this problem, namely label ranking with a reject option. Just like in conventional classification, where a classifier can refuse a presumably unreliable prediction, the idea is to concede a label ranker the possibility to abstain. More specifically, the label ranker is allowed to make a weaker prediction in the form of a partial instead of a total order. Thus, unlike a conventional classifier which either makes a prediction or not, a label ranker can abstain to a certain degree. To realize label ranking with a reject option, we propose a method based on ensemble learning techniques. First empirical results are presented showing great promise for the usefulness of the approach.

اسناد مشابه

  • Label Ranking with Abstention: Predicting Partial Orders by Thresholding Probability Distributions (Extended Abstract)

    سند علمی۲۰۱۱CoRRWeiwei Cheng, Eyke Hüllermeier

    We consider an extension of the setting of label ranking, in which the learner is allowed to make predictions in the form of partial instead of total orders. Predictions of that kind are interpreted as a partial abstention: If the learner is not sufficiently certain regarding the

  • Label Ranking with Abstention: Predicting Partial Orders by Thresholding Probability Distributions

    سند علمی۲۰۱۱Weiwei Cheng, Eyke Hüllermeier

    We consider an extension of the setting of label ranking, in which the learner is allowed to make predictions in the form of partial instead of total orders. Predictions of that kind are interpreted as a partial abstention: If the learner is not sufficiently certain regarding the

  • Comparing Two Approaches for Adding Feature Ranking to Sampled Ensemble Learning for Software Quality Estimation

    سند علمی۲۰۱۴SEKEKehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano

    High dimensionality and class imbalance are two main problems that affect the quality of training datasets in software defect prediction, resulting in inefficient classification models. Feature selection and data sampling are often used to overcome these problems. Feature selecti

  • Label Ranking with Semi-Supervised Learning

    سند علمی۲۰۱۰Austr. J. Intelligent Information Processing SystemsWei Wang, Irwin King

    Label ranking is considered as an efficient approach for object recognition, document classification, recommendation task, which has been widely studied in recent years. It aims to learn a mapping from instances to a ranking list over a finite set of predefined labels. Traditiona

  • Label Ranking with Partial Abstention based on Thresholded Probabilistic Models

    سند علمی۲۰۱۲NIPSWeiwei Cheng, Eyke Hüllermeier, Willem Waegeman, Volkmar Welker

    Several machine learning methods allow for abstaining from uncertain predictions. While being common for settings like conventional classification, abstention has been studied much less in learning to rank. We address abstention for the label ranking setting, allowing the learner

  • Predicting Partial Orders: Ranking with Abstention

    سند علمی۲۰۱۰ECML/PKDDWeiwei Cheng, Michaël Rademaker, Bernard De Baets, Eyke Hüllermeier

    The prediction of structured outputs in general and rankings in particular has attracted considerable attention in machine learning in recent years, and different types of ranking problems have already been studied. In this paper, we propose a generalization or, say, relaxation o