41,356 research outputs found
An Overview of Classifier Fusion Methods
A number of classifier fusion methods have been
recently developed opening an alternative approach
leading to a potential improvement in the
classification performance. As there is little theory of
information fusion itself, currently we are faced with
different methods designed for different problems and
producing different results. This paper gives an
overview of classifier fusion methods and attempts to
identify new trends that may dominate this area of
research in future. A taxonomy of fusion methods
trying to bring some order into the existing âpudding
of diversitiesâ is also provided
An Overview of Classifier Fusion Methods
A number of classifier fusion methods have been
recently developed opening an alternative approach
leading to a potential improvement in the
classification performance. As there is little theory of
information fusion itself, currently we are faced with
different methods designed for different problems and
producing different results. This paper gives an
overview of classifier fusion methods and attempts to
identify new trends that may dominate this area of
research in future. A taxonomy of fusion methods
trying to bring some order into the existing âpudding
of diversitiesâ is also provided
Can we measure hospital quality from physicians' choices?
In this paper, we propose an alternative methodology for ranking hospitals based on the choices of Medical School graduates over hospital training vacancies. Our methodology is therefore a revealed preference approach. Our methodology for measuring relative hospital quality has the following desirable properties: a) robust to manipulation from hospital administrators; b) conditional on having enough observations, it allows for differences in quality across specialties within a hospital; c) inexpensive in terms of data requirements, d) not subject to selection bias from patients nor hospital screening of patients; and e) unlike other rankings based on experts' evaluations, it does not require physicians to provide a complete ranking of all hospitals. We apply our methodology to the Spanish case and find, among other results, the following: First, the probability of choosing the best hospital relative to the worst hospital is statistically significantly different from zero. Second, physicians value proximity and nearby hospitals are seen as more substitutable. Third, observable time-invariant city characteristics are unrelated to results. Finally, our estimates for physicians' hospital valuations are significantly correlated to more traditional hospital quality measures
Some Remarks about the Complexity of Epidemics Management
Recent outbreaks of Ebola, H1N1 and other infectious diseases have shown that
the assumptions underlying the established theory of epidemics management are
too idealistic. For an improvement of procedures and organizations involved in
fighting epidemics, extended models of epidemics management are required. The
necessary extensions consist in a representation of the management loop and the
potential frictions influencing the loop. The effects of the non-deterministic
frictions can be taken into account by including the measures of robustness and
risk in the assessment of management options. Thus, besides of the increased
structural complexity resulting from the model extensions, the computational
complexity of the task of epidemics management - interpreted as an optimization
problem - is increased as well. This is a serious obstacle for analyzing the
model and may require an additional pre-processing enabling a simplification of
the analysis process. The paper closes with an outlook discussing some
forthcoming problems
A kernel-based framework for learning graded relations from data
Driven by a large number of potential applications in areas like
bioinformatics, information retrieval and social network analysis, the problem
setting of inferring relations between pairs of data objects has recently been
investigated quite intensively in the machine learning community. To this end,
current approaches typically consider datasets containing crisp relations, so
that standard classification methods can be adopted. However, relations between
objects like similarities and preferences are often expressed in a graded
manner in real-world applications. A general kernel-based framework for
learning relations from data is introduced here. It extends existing approaches
because both crisp and graded relations are considered, and it unifies existing
approaches because different types of graded relations can be modeled,
including symmetric and reciprocal relations. This framework establishes
important links between recent developments in fuzzy set theory and machine
learning. Its usefulness is demonstrated through various experiments on
synthetic and real-world data.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
- âŠ