57 research outputs found
Projection based ensemble learning for ordinal regression
The classification of patterns into naturally ordered
labels is referred to as ordinal regression. This paper proposes
an ensemble methodology specifically adapted to this type of
problems, which is based on computing different classification
tasks through the formulation of different order hypotheses.
Every single model is trained in order to distinguish between
one given class (k) and all the remaining ones, but grouping
them in those classes with a rank lower than k, and those
with a rank higher than k. Therefore, it can be considered as
a reformulation of the well-known one-versus-all scheme. The
base algorithm for the ensemble could be any threshold (or
even probabilistic) method, such as the ones selected in this
paper: kernel discriminant analysis, support vector machines
and logistic regression (all reformulated to deal with ordinal
regression problems). The method is seen to be competitive when
compared with other state-of-the-art methodologies (both ordinal
and nominal), by using six measures and a total of fifteen ordinal
datasets. Furthermore, an additional set of experiments is used to
study the potential scalability and interpretability of the proposed
method when using logistic regression as base methodology for
the ensemble
Borderline kernel based over-sampling
Nowadays, the imbalanced nature of some real-world data
is receiving a lot of attention from the pattern recognition and machine
learning communities in both theoretical and practical aspects, giving
rise to di erent promising approaches to handling it. However, preprocessing
methods operate in the original input space, presenting distortions
when combined with kernel classi ers, that operate in the feature
space induced by a kernel function. This paper explores the notion of
empirical feature space (a Euclidean space which is isomorphic to the feature
space and therefore preserves its structure) to derive a kernel-based
synthetic over-sampling technique based on borderline instances which
are considered as crucial for establishing the decision boundary. Therefore,
the proposed methodology would maintain the main properties of
the kernel mapping while reinforcing the decision boundaries induced by
a kernel machine. The results show that the proposed method achieves
better results than the same borderline over- sampling method applied
in the original input spac
Situated learning and education: development and validation of the future teacher attitudes scale in the application of augmented reality in the classroom
This research article focuses on the design and validation of a questionnaire to analyse future teachers' perceptions of professional skills through the use of Augmented Reality (AR) in higher education, specifically for students in the field of Educational Sciences. The sample consisted of 575 students of Early Childhood Education, Primary Education and Pedagogy during the academic year (2021/2022). The focus of this study is to authenticate a questionnaire that measures the influence of Augmented Reality (AR) on aspects such as situated learning, motivation, and the necessary instructional preparations for the successful integration of AR within classroom educational encounters. The questionnaire is an online Likert-type scale developed based on three dimensions: situated learning, motivation and training. The data were analysed using the Statistical Package for the Social Sciences (SPSS) version 25 and JASP 0.17.1. The questionnaire met the standards recommended for validation. However, improvements to the instrument are suggested. In conclusion, validation of instruments is necessary to gain a rigorous understanding of the impact of new learning environments
Ordinal regression methods: survey and experimental study
Abstract—Ordinal regression problems are those machine learning problems where the objective is to classify patterns using a
categorical scale which shows a natural order between the labels. Many real-world applications present this labelling structure and
that has increased the number of methods and algorithms developed over the last years in this field. Although ordinal regression can
be faced using standard nominal classification techniques, there are several algorithms which can specifically benefit from the ordering
information. Therefore, this paper is aimed at reviewing the state of the art on these techniques and proposing a taxonomy based on
how the models are constructed to take the order into account. Furthermore, a thorough experimental study is proposed to check if
the use of the order information improves the performance of the models obtained, considering some of the approaches within the
taxonomy. The results confirm that ordering information benefits ordinal models improving their accuracy and the closeness of the
predictions to actual targets in the ordinal scal
A weed monitoring system using UAV-imagery and the Hough transform
Usually, crops require the use of herbicides as a useful manner of controlling the
quality and quantity of crop production. Although there are weed-free areas, the most
common approach is to broadcast herbicides entirely over crop fields, resulting in a
reduction of profits and increase in environmental risks. Recently, patch spraying has
allowed the use of site-specific weed management, allowing precise and timely weed maps at
very early phenological stage, either by ground sampling or remote analysis. Remote imagery
from piloted planes and satellites are not suitable for this purpose given their low spatial and
temporal resolutions, however, unmanned aerial vehicles (UAV) represent an excellent
alternative. This paper presents a new classification framework for weed monitoring via UAV
showing promising results and accurate generalisation in different scenariosLos cultivos precisan del uso de herbicidas para controlar la calidad y cantidad
de producción. A pesar de que las malas hierbas se distribuyen en rodales, la práctica más
extendida es la fumigación de herbicidas en todo el cultivo, resultando en un aumento del
coste y de riesgos mediambientales. La pulvericación por parches ha dado lugar al auge de
otras técnicas de manejo de malas hierbas, permitiendo su tratamiento en un estado
fenológico temprano. Las imágenes remotas de aviones pilotados o satélites no son útiles en
este caso debido a su baja resolución espacial y temporal. Sin embargo, este no es el caso de
los vehículos aéreos no tripulados. Este artículo presenta un nuevo método para
monitorización de malas hierbas usando este tipo de vehículos, mostrando resultados
prometedore
Object-Based Image Classification of Summer Crop with Machine Learning Methods
The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous) from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86%) and notably higher than C4.5 (79%). The SVM+SVM classifier (best method) improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity). The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification tasks.This research was partly financed by the TIN2011-22794 project of the Spanish Ministerial
Commission of Science and Technology (MICYT), FEDER funds, the P2011-TIC-7508 project of the
“Junta de Andalucía” (Spain) and the Kearney Foundation of Soil Science (USA). The research of
Peña was co-financed by the Fulbright-MEC postdoctoral program, financed by the Spanish Ministry
for Science and Innovation, and by the JAEDoc Program, supported by CSIC and FEDER funds.
ASTER data were available to us through a NASA EOS scientific investigator affiliation.We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI).Peer Reviewe
A Review of Classification Problems and Algorithms in Renewable Energy Applications
Classification problems and their corresponding solving approaches constitute one of the
fields of machine learning. The application of classification schemes in Renewable Energy (RE) has
gained significant attention in the last few years, contributing to the deployment, management and
optimization of RE systems. The main objective of this paper is to review the most important
classification algorithms applied to RE problems, including both classical and novel algorithms.
The paper also provides a comprehensive literature review and discussion on different classification
techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in
RE systems, power quality disturbance classification and other applications in alternative RE systems.
In this way, the paper describes classification techniques and metrics applied to RE problems,
thus being useful both for researchers dealing with this kind of problem and for practitioners
of the field
Semi-supervised Learning for Ordinal Kernel Discriminant Analysis
Ordinal classication considers those classication problems where the labels of
the variable to predict follow a given order. Naturally, labelled data is scarce
or di_cult to obtain in this type of problems because, in many cases, ordinal
labels are given by an user or expert (e.g. in recommendation systems). Firstly,
this paper develops a new strategy for ordinal classi_cation where both labelled
and unlabelled data are used in the model construction step (a scheme which
is referred to as semi-supervised learning). More specically, the ordinal version
of kernel discriminant learning is extended for this setting considering the
neighbourhood information of unlabelled data, which is proposed to be computed
in the feature space induced by the kernel function. Secondly, a new
method for semi-supervised kernel learning is devised in the context of ordinal
classi_cation, which is combined with our developed classi_cation strategy to
optimise the kernel parameters. The experiments conducted compare 6 different
approaches for semi-supervised learning in the context of ordinal classication
in a battery of 30 datasets, showing 1) the good synergy of the ordinal version
of discriminant analysis and the use of unlabelled data and 2) the advantage of
computing distances in the feature space induced by the kernel function
Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery
This paper approaches the problem of weed mapping for precision agriculture,
using imagery provided by Unmanned Aerial Vehicles (UAVs) from sun
ower
and maize crops. Precision agriculture referred to weed control is mainly based
on the design of early post-emergence site-speci c control treatments according
to weed coverage, where one of the most important challenges is the spectral
similarity of crop and weed pixels in early growth stages. Our work tackles
this problem in the context of object-based image analysis (OBIA) by means
of supervised machine learning methods combined with pattern and feature
selection techniques, devising a strategy for alleviating the user intervention in
the system while not compromising the accuracy. This work rstly proposes
a method for choosing a set of training patterns via clustering techniques so
as to consider a representative set of the whole eld data spectrum for the
classi cation method. Furthermore, a feature selection method is used to obtain
the best discriminating features from a set of several statistics and measures of
di erent nature. Results from this research show that the proposed method for
pattern selection is suitable and leads to the construction of robust sets of data.
The exploitation of di erent statistical, spatial and texture metrics represents a
new avenue with huge potential for between and within crop-row weed mapping
via UAV-imagery and shows good synergy when complemented with OBIA.
Finally, there are some measures (specially those linked to vegetation indexes)
that are of great in
uence for weed mapping in both sun
ower and maize crop
Evaluación de la influencia de los recursos computacionales en la QoE del servicio.
The new generation of mobile networks goes beyond radio communications by providing a resilient and flexible architecture. In this context, the virtualization of Radio Access Networks (vRAN) completes the Network Func4on Virtualization (NFV) milestone, enabling a distributed and scalable network architecture. However, this approach increases the complexity of management tasks as computing resources start to play an essential role in the network provisioning process. In this sense, this work aims to assess the impact of computational resources on the delivery of video streaming services. The results obtained prove that inadequate resource assignment to vRAN instances leads to degradation of the Quality of Experience (QoE), even if the allocation of radio resources is adequate for the service.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
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