58,709 research outputs found
Evaluating epistemic uncertainty under incomplete assessments
The thesis of this study is to propose an extended methodology for laboratory based Information Retrieval evaluation under incomplete relevance assessments. This new methodology aims to identify potential uncertainty during system comparison that may result from incompleteness. The adoption of this methodology is advantageous, because the detection of epistemic uncertainty - the amount of knowledge (or ignorance) we have about the estimate of a system's performance - during the evaluation process can guide and direct researchers when evaluating new systems over existing and future test collections. Across a series of experiments we demonstrate how this methodology can lead towards a finer grained analysis of systems. In particular, we show through experimentation how the current practice in Information Retrieval evaluation of using a measurement depth larger than the pooling depth increases uncertainty during system comparison
Probabilistic performance estimators for computational chemistry methods: Systematic Improvement Probability and Ranking Probability Matrix. I. Theory
The comparison of benchmark error sets is an essential tool for the
evaluation of theories in computational chemistry. The standard ranking of
methods by their Mean Unsigned Error is unsatisfactory for several reasons
linked to the non-normality of the error distributions and the presence of
underlying trends. Complementary statistics have recently been proposed to
palliate such deficiencies, such as quantiles of the absolute errors
distribution or the mean prediction uncertainty. We introduce here a new score,
the systematic improvement probability (SIP), based on the direct system-wise
comparison of absolute errors. Independently of the chosen scoring rule, the
uncertainty of the statistics due to the incompleteness of the benchmark data
sets is also generally overlooked. However, this uncertainty is essential to
appreciate the robustness of rankings. In the present article, we develop two
indicators based on robust statistics to address this problem: P_{inv}, the
inversion probability between two values of a statistic, and \mathbf{P}_{r},
the ranking probability matrix. We demonstrate also the essential contribution
of the correlations between error sets in these scores comparisons
Face image matching using fractal dimension
A new method is presented in this paper for calculating the correspondence between two face images on a pixel by pixel basis. The concept of fractal dimension is used to develop the proposed non-parametric area-based image matching method which achieves a higher proportion of matched pixels for face images than some well-known methods
The Efficiency of Private Universities As Measured By Graduation Rates
It is well known that human capital is enhanced by graduation from a college or university. How efficient are such institutions in conveying this mark of human capital? Efficiency and productivity in private higher education is measured by using undergraduate graduation rates as the output, and demographic variables, the quality of the students, and the annual expenditures (adjusted for academic mission) as inputs. Tests of several models using OLS and stochastic frontier analysis confirm that private schools can increase their graduation rates by increasing focused expenditures and through more selective admissions. Estimated elasticities are reported and point toward increasing expenditures as the most responsive method. Estimate graduation efficiencies of 93.0, 91.5, and near 100% are also reported for four, five and six year graduation rates respectively. A rank correlation with the U S News and World Report 2008 rankings is consistent with our measure of relative efficiencies
Towards better measures: evaluation of estimated resource description quality for distributed IR
An open problem for Distributed Information Retrieval systems (DIR) is how to represent large document repositories, also known as resources, both accurately and efficiently. Obtaining resource description estimates is an important phase in DIR, especially in non-cooperative environments. Measuring the quality of an estimated resource description is a contentious issue as current measures do not provide an adequate indication of quality. In this paper, we provide an overview of these currently applied measures of resource description quality, before proposing the Kullback-Leibler (KL) divergence as an alternative. Through experimentation we illustrate the shortcomings of these past measures, whilst providing evidence that KL is a more appropriate measure of quality. When applying KL to compare different QBS algorithms, our experiments provide strong evidence in favour of a previously unsupported hypothesis originally posited in the initial Query-Based Sampling work
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
Recommendation systems are ubiquitous and impact many domains; they have the
potential to influence product consumption, individuals' perceptions of the
world, and life-altering decisions. These systems are often evaluated or
trained with data from users already exposed to algorithmic recommendations;
this creates a pernicious feedback loop. Using simulations, we demonstrate how
using data confounded in this way homogenizes user behavior without increasing
utility
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