4,062 research outputs found
Optimal Data Collection For Informative Rankings Expose Well-Connected Graphs
Given a graph where vertices represent alternatives and arcs represent
pairwise comparison data, the statistical ranking problem is to find a
potential function, defined on the vertices, such that the gradient of the
potential function agrees with the pairwise comparisons. Our goal in this paper
is to develop a method for collecting data for which the least squares
estimator for the ranking problem has maximal Fisher information. Our approach,
based on experimental design, is to view data collection as a bi-level
optimization problem where the inner problem is the ranking problem and the
outer problem is to identify data which maximizes the informativeness of the
ranking. Under certain assumptions, the data collection problem decouples,
reducing to a problem of finding multigraphs with large algebraic connectivity.
This reduction of the data collection problem to graph-theoretic questions is
one of the primary contributions of this work. As an application, we study the
Yahoo! Movie user rating dataset and demonstrate that the addition of a small
number of well-chosen pairwise comparisons can significantly increase the
Fisher informativeness of the ranking. As another application, we study the
2011-12 NCAA football schedule and propose schedules with the same number of
games which are significantly more informative. Using spectral clustering
methods to identify highly-connected communities within the division, we argue
that the NCAA could improve its notoriously poor rankings by simply scheduling
more out-of-conference games.Comment: 31 pages, 10 figures, 3 table
Matrices of forests, analysis of networks, and ranking problems
The matrices of spanning rooted forests are studied as a tool for analysing
the structure of networks and measuring their properties. The problems of
revealing the basic bicomponents, measuring vertex proximity, and ranking from
preference relations / sports competitions are considered. It is shown that the
vertex accessibility measure based on spanning forests has a number of
desirable properties. An interpretation for the stochastic matrix of
out-forests in terms of information dissemination is given.Comment: 8 pages. This article draws heavily from arXiv:math/0508171.
Published in Proceedings of the First International Conference on Information
Technology and Quantitative Management (ITQM 2013). This version contains
some corrections and addition
Fitness Uniform Optimization
In evolutionary algorithms, the fitness of a population increases with time
by mutating and recombining individuals and by a biased selection of more fit
individuals. The right selection pressure is critical in ensuring sufficient
optimization progress on the one hand and in preserving genetic diversity to be
able to escape from local optima on the other hand. Motivated by a universal
similarity relation on the individuals, we propose a new selection scheme,
which is uniform in the fitness values. It generates selection pressure toward
sparsely populated fitness regions, not necessarily toward higher fitness, as
is the case for all other selection schemes. We show analytically on a simple
example that the new selection scheme can be much more effective than standard
selection schemes. We also propose a new deletion scheme which achieves a
similar result via deletion and show how such a scheme preserves genetic
diversity more effectively than standard approaches. We compare the performance
of the new schemes to tournament selection and random deletion on an artificial
deceptive problem and a range of NP-hard problems: traveling salesman, set
covering and satisfiability.Comment: 25 double-column pages, 12 figure
Tournament versus Fitness Uniform Selection
In evolutionary algorithms a critical parameter that must be tuned is that of
selection pressure. If it is set too low then the rate of convergence towards
the optimum is likely to be slow. Alternatively if the selection pressure is
set too high the system is likely to become stuck in a local optimum due to a
loss of diversity in the population. The recent Fitness Uniform Selection
Scheme (FUSS) is a conceptually simple but somewhat radical approach to
addressing this problem - rather than biasing the selection towards higher
fitness, FUSS biases selection towards sparsely populated fitness levels. In
this paper we compare the relative performance of FUSS with the well known
tournament selection scheme on a range of problems.Comment: 10 pages, 8 figure
Search Engine Optimisation in UK news production
This is an Author's Accepted Manuscript of an article published in Journalism Practice, 5(4), 462 - 477, 2011, copyright Taylor & Francis, available online at: http://www.tandfonline.com/10.1080/17512786.2010.551020.This paper represents an exploratory study into an emerging culture in UK online newsrooms—the practice of Search Engine Optimisation (SEO), which assesses its impact on news production. Comprising a short-term participant observational case study at a national online news publisher, and a series of semi-structured, in-depth interviews with SEO professionals at three further UK media organisations, the author sets out to establish how SEO is operationalised in the newsroom, and what consequences these practices have for online news production. SEO practice is found to be varied and application is not universal. Not all UK news organisations are making the most of SEO even though some publishers take a highly sophisticated approach. Efforts are constrained by time, resources and management support, as well as off-page technical issues. SEO policy is found, in some cases, to inform editorial policy, but there is resistance to the principal of SEO driving decision-making. Several themes are established which call for further research
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