6,024 research outputs found
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
Models for Paired Comparison Data: A Review with Emphasis on Dependent Data
Thurstonian and Bradley-Terry models are the most commonly applied models in
the analysis of paired comparison data. Since their introduction, numerous
developments have been proposed in different areas. This paper provides an
updated overview of these extensions, including how to account for object- and
subject-specific covariates and how to deal with ordinal paired comparison
data. Special emphasis is given to models for dependent comparisons. Although
these models are more realistic, their use is complicated by numerical
difficulties. We therefore concentrate on implementation issues. In particular,
a pairwise likelihood approach is explored for models for dependent paired
comparison data, and a simulation study is carried out to compare the
performance of maximum pairwise likelihood with other limited information
estimation methods. The methodology is illustrated throughout using a real data
set about university paired comparisons performed by students.Comment: Published in at http://dx.doi.org/10.1214/12-STS396 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Don't classify ratings of affect ; rank them!
How should affect be appropriately annotated and how should machine learning best be employed to map
manifestations of affect to affect annotations? What is the use of ratings of affect for the study of affective computing and
how should we treat them? These are the key questions this paper attempts to address by investigating the impact of dissimilar
representations of annotated affect on the efficacy of affect modelling. In particular, we compare several different binary-class
and pairwise preference representations for automatically learning from ratings of affect. The representations are compared and
tested on three datasets: one synthetic dataset (testing “in vitro”) and two affective datasets (testing “in vivo”). The synthetic
dataset couples a number of attributes with generated rating values. The two affective datasets contain physiological and
contextual user attributes, and speech attributes, respectively; these attributes are coupled with ratings of various affective
and cognitive states. The main results of the paper suggest that ratings (when used) should be naturally transformed to ordinal
(ranked) representations for obtaining more reliable and generalisable models of affect. The findings of this paper have a direct
impact on affect annotation and modelling research but, most importantly, challenge the traditional state-of-practice in affective
computing and psychometrics at large.peer-reviewe
Testing Measurement Invariance with Ordinal Missing Data: A Comparison of Estimators and Missing Data Techniques
Ordinal missing data are common in measurement equivalence/invariance (ME/I) testing studies. However, there is a lack of guidance on the appropriate method to deal with ordinal missing data in ME/I testing. Five methods may be used to deal with ordinal missing data in ME/I testing, including the continuous full information maximum likelihood estimation method (FIML), continuous robust FIML (rFIML), FIML with probit links (pFIML), FIML with logit links (lFIML), and mean and variance adjusted weight least squared estimation method combined with pairwise deletion (WLSMV_PD). The current study evaluates the relative performance of these methods in producing valid chi-square difference tests (Δχ2) and accurate parameter estimates. The result suggests that all methods except for WLSMV_PD can reasonably control the type I error rates of (Δχ2) tests and maintain sufficient power to detect noninvariance in most conditions. Only pFIML and lFIML yield accurate factor loading estimates and standard errors across all the conditions. Recommendations are provided to researchers based on the results
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