Pairwise comparison

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

Using the analytic hierarchy process in evaluating decision alternatives

Abstract: In this paper the method of the Analytic Hierarchy Process (AHP) is described. At the beginning the general assumptions of the method are characterized and discussed. These are related to assumptions held within General Systems Theory. Then the problems of pairwise comparison of elements, with its use of a specific scale, as well as the resulting reciprocal matrix are presented. There are many ways of estimating the eigenvectors of this matrix. These eigenvectors reflects weights of preferences. Despite the fact that we are able to evaluate the consistency of our judgments, the problem of acceptable weights still remains. Therefore, by way of an illustration, a method for the sensitivity analysis of preferences is also discussed in the paper.

Multiple graph regularized protein domain ranking

Abstract: Background Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods.

A logic-based approach to combinatorial testing with constraints

Abstract: Usage of combinatorial testing is wide spreading as an effective technique to reveal unintended feature interaction inside a given system. To this aim, test cases are constructed by combining tuples of assignments of the different input parameters, based on some effective combinatorial strategy. The most commonly used strategy is two-way (pairwise) coverage, requiring all combinations of valid assignments for all possible pairs of input parameters to be covered by at least one test case. In this paper a new heuristic strategy developed for the construction of pairwise covering test suites is presented, featuring a new approach to support expressive constraining over the input domain. Moreover, it allows the inclusion or exclusion of ad-hoc combinations of parameter bindings to let the user customize the test suite outcome. Our approach is tightly integrated with formal logic, since it uses test predicates to formalize combinatorial testing as a logic problem, and applies an external model checker tool to solve it. The proposed approach is supported by a prototype tool implementation, and early results of experimental assessment are also presented.

Learning user preferences by adaptive pairwise comparison

Abstract: Users make choices among multi-attribute objects in a data set in a variety of domains including used car purchase, job search and hotel room booking. Individual users sometimes have strong preferences between objects, but these preferences may not be universally shared by all users. If we can cast these preferences as derived from a quantitative user-specific preference function, then we can predict user preferences by learning their preference function, even though the preference function itself is not directly observable, and may be hard to express.In this paper we study the problem of preference learning with pairwise comparisons on a set of entities with multiple attributes. We formalize the problem into two subproblems, namely preference estimation and comparison selection. We propose an innovative approach to estimate the preference, and introduce a binary search strategy to adaptively select the comparisons. We introduce the concept of an orthogonal query to support this adaptive selection, as well as a novel S-tree index to enable efficient evaluation of orthogonal queries.We integrate these components into a system for inferring user preference with adaptive pairwise comparisons. Our experiments and user study demonstrate that our adaptive system significantly outperforms the naive random selection system on both real data and synthetic data, with either simulated or real user feedback. We also show our preference learning approach is much more effective than existing approaches, and our S-tree can be constructed efficiently and perform orthogonal query at interactive speeds.