Pairwise comparison

About: Pairwise comparison is a research topic. Over the lifetime, 6804 publications have been published within this topic receiving 174081 citations.

A Unified Model for Multilabel Classification and Ranking

Abstract: Label ranking studies the problem of learning a mapping from instances to rankings over a predefined set of labels. Hitherto existing approaches to label ranking implicitly operate on an underlying (utility) scale which is not calibrated in the sense that it lacks a natural zero point. We propose a suitable extension of label ranking that incorporates the calibrated scenario and substantially extends the expressive power of these approaches. In particular, our extension suggests a conceptually novel technique for extending the common learning by pairwise comparison approach to the multilabel scenario, a setting previously not being amenable to the pairwise decomposition technique. We present empirical results in the area of text categorization and gene analysis, underscoring the merits of the calibrated model in comparison to state-of-the-art multilabel learning methods.

Computing slater rankings using similarities among candidates

Abstract: Voting (or rank aggregation) is a general method for aggregating the preferences of multiple agents One important voting rule is the Slater rule It selects a ranking of the alternatives (or candidates) to minimize the number of pairs of candidates such that the ranking disagrees with the pairwise majority vote on these two candidates The use of the Slater rule has been hindered by a lack of techniques to compute Slater rankings In this paper, we show how we can decompose the Slater problem into smaller subproblems if there is a set of similar candidates We show that this technique suffices to compute a Slater ranking in linear time if the pairwise majority graph is hierarchically structured For the general case, we also give an efficient algorithm for finding a set of similar candidates We provide experimental results that show that this technique significantly (sometimes drastically) speeds up search algorithms, Finally, we also use the technique of similar sets to show that computing an optimal Slater ranking is NP-hard even in the absence of pairwise ties

ANP/RPN: a multi criteria evaluation of the Risk Priority Number

Abstract: This paper presents an advanced version of the failure mode effects and criticality analysis (FMECA), whose capabilities are enhanced; in that the criticality assessment takes into account possible interactions among the principal causes of failure. This is obtained by integrating FMECA and Analytic Network Process, a multi-criteria decision making technique. Severity, Occurrence and Detectability are split into sub-criteria and arranged in a hybrid (hierarchy/network) decision-structure that, at the lowest level, contains the causes of failure. Starting from this decision-structure, the Risk Priority Number is computed making pairwise comparisons, so that qualitative judgements and reliable quantitative data can be easily included in the analysis, without using vague and unreliable linguistic conversion tables. Pairwise comparison also facilitates the effort of the design/maintenance team, since it is easier to place comparative rather than absolute judgments, to quantify the importance of the causes of failure. In order to clarify and to make evident the rational of the final results, a graphical tool, similar to the House of Quality, is also presented. At the end of the paper, a case study, which confirms the quality of the approach and shows its capability to perform robust and comprehensive criticality analyses, is reported. Copyright © 2011 John Wiley and Sons Ltd.

An overview on predicting the subcellular location of a protein.

Abstract: The present paper overviews the issue on predicting the subcellular location of a protein. Five measures of extracting information from the global sequence based on the Bayes discriminant algorithm are reviewed. 1) The auto-correlation functions of amino acid indices along the sequence; 2) The quasi-sequence-order approach; 3) the pseudo-amino acid composition; 4) the unified attribute vector in Hilbert space, 5) Zp parameters extracted from the Zp curve. The actual performance of the predictive accuracy is closely related to the degree of similarity between the training and testing sets or to the average degree of pairwise similarity in dataset in a cross-validated study. Many scholars considered that the current higher predictive accuracy still cannot ensure that some available algorithms are effective in practice prediction for the higher pairwise sequence identity of the datasets, but some of them declared that construction of the dataset used for developing software should base on the reality determined by the Mother Nature that some subcellular locations really contain only a minor number of proteins of which some even have a high percentage of sequence similarity. Owing to the complexity of the problem itself, some very sophisticated and special programs are needed for both constructing dataset and improving the prediction. Anyhow finding the target information in mature protein sequence and properly cooperating it with sorting signals in prediction may further improve the overall predictive accuracy and make the prediction into practice.