13,570 research outputs found
On the characterization of flowering curves using Gaussian mixture models
In this paper, we develop a statistical methodology applied to the
characterization of flowering curves using Gaussian mixture models. Our study
relies on a set of rosebushes flowering data, and Gaussian mixture models are
mainly used to quantify the reblooming properties of each one. In this regard,
we also suggest our own selection criterion to take into account the lack of
symmetry of most of the flowering curves. Three classes are created on the
basis of a principal component analysis conducted on a set of reblooming
indicators, and a subclassification is made using a longitudinal --means
algorithm which also highlights the role played by the precocity of the
flowering. In this way, we obtain an overview of the correlations between the
features we decided to retain on each curve. In particular, results suggest the
lack of correlation between reblooming and flowering precocity. The pertinent
indicators obtained in this study will be a first step towards the
comprehension of the environmental and genetic control of these biological
processes.Comment: 28 pages, 27 figure
Benchmark of structured machine learning methods for microbial identification from mass-spectrometry data
Microbial identification is a central issue in microbiology, in particular in
the fields of infectious diseases diagnosis and industrial quality control. The
concept of species is tightly linked to the concept of biological and clinical
classification where the proximity between species is generally measured in
terms of evolutionary distances and/or clinical phenotypes. Surprisingly, the
information provided by this well-known hierarchical structure is rarely used
by machine learning-based automatic microbial identification systems.
Structured machine learning methods were recently proposed for taking into
account the structure embedded in a hierarchy and using it as additional a
priori information, and could therefore allow to improve microbial
identification systems. We test and compare several state-of-the-art machine
learning methods for microbial identification on a new Matrix-Assisted Laser
Desorption/Ionization Time-of-Flight mass spectrometry (MALDI-TOF MS) dataset.
We include in the benchmark standard and structured methods, that leverage the
knowledge of the underlying hierarchical structure in the learning process. Our
results show that although some methods perform better than others, structured
methods do not consistently perform better than their "flat" counterparts. We
postulate that this is partly due to the fact that standard methods already
reach a high level of accuracy in this context, and that they mainly confuse
species close to each other in the tree, a case where using the known hierarchy
is not helpful
The Antisymmetry Betweenness Axiom and Hausdorff Continua
An interpretation of betweenness on a set satisfies the antisymmetry axiom at a point a if it is impossible for each of two distinct points to lie between the other and a. In this paper we study the role of antisymmetry as it applies to the K-interpretation of betweenness in a Hausdorff continuum X, where a point c lies between points a and b exactly when every subcontinuum of X containing both a and b contains c as well
Feature-Based Diversity Optimization for Problem Instance Classification
Understanding the behaviour of heuristic search methods is a challenge. This
even holds for simple local search methods such as 2-OPT for the Traveling
Salesperson problem. In this paper, we present a general framework that is able
to construct a diverse set of instances that are hard or easy for a given
search heuristic. Such a diverse set is obtained by using an evolutionary
algorithm for constructing hard or easy instances that are diverse with respect
to different features of the underlying problem. Examining the constructed
instance sets, we show that many combinations of two or three features give a
good classification of the TSP instances in terms of whether they are hard to
be solved by 2-OPT.Comment: 20 pages, 18 figure
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