This paper proposes a novel approach to multilabel classification called calibrated label ranking. The key idea is to introduce an artificial calibration label that separates relevant from irrelevant labels in each example. This technique combines pairwise preference learning with conventional relevance classification, where a separate classifier is trained to predict whether a label is relevant or not. The calibration label allows for a more accurate representation of the relationship between labels and enables the extension of pairwise comparison methods to the multilabel scenario. The approach is shown to be effective in text categorization, image classification, and gene analysis tasks. The paper also discusses the computational complexity of the approach and presents empirical results demonstrating its superiority over state-of-the-art multilabel learning methods. The calibrated label ranking approach is shown to outperform conventional pairwise ranking methods in terms of both ranking performance and calibration accuracy. The results indicate that the calibrated approach provides a more accurate and reliable way to handle multilabel classification tasks.This paper proposes a novel approach to multilabel classification called calibrated label ranking. The key idea is to introduce an artificial calibration label that separates relevant from irrelevant labels in each example. This technique combines pairwise preference learning with conventional relevance classification, where a separate classifier is trained to predict whether a label is relevant or not. The calibration label allows for a more accurate representation of the relationship between labels and enables the extension of pairwise comparison methods to the multilabel scenario. The approach is shown to be effective in text categorization, image classification, and gene analysis tasks. The paper also discusses the computational complexity of the approach and presents empirical results demonstrating its superiority over state-of-the-art multilabel learning methods. The calibrated label ranking approach is shown to outperform conventional pairwise ranking methods in terms of both ranking performance and calibration accuracy. The results indicate that the calibrated approach provides a more accurate and reliable way to handle multilabel classification tasks.