2014
O'Mahony, Niamh; Florentino-Liaño, Blanca; Carballo, Juan J; Baca-García, Enrique; Artés-Rodríguez, Antonio
Objective diagnosis of ADHD using IMUs Artículo de revista
En: Medical engineering & physics, vol. 36, no 7, pp. 922–6, 2014, ISSN: 1873-4030.
Resumen | Enlaces | BibTeX | Etiquetas: Attention deficit/hyperactivity disorder, Classification, Inertial sensors, Machine learning, Objective diagnosis
@article{O'Mahony2014,
title = {Objective diagnosis of ADHD using IMUs},
author = {Niamh O'Mahony and Blanca Florentino-Lia\~{n}o and Juan J Carballo and Enrique Baca-Garc\'{i}a and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.tsc.uc3m.es/~antonio/papers/P50_2014_Objective Diagnosis of ADHD Using IMUs.pdf
http://www.sciencedirect.com/science/article/pii/S1350453314000459},
issn = {1873-4030},
year = {2014},
date = {2014-01-01},
journal = {Medical engineering \& physics},
volume = {36},
number = {7},
pages = {922--6},
abstract = {This work proposes the use of miniature wireless inertial sensors as an objective tool for the diagnosis of ADHD. The sensors, consisting of both accelerometers and gyroscopes to measure linear and rotational movement, respectively, are used to characterize the motion of subjects in the setting of a psychiatric consultancy. A support vector machine is used to classify a group of subjects as either ADHD or non-ADHD and a classification accuracy of greater than 95% has been achieved. Separate analyses of the motion data recorded during various activities throughout the visit to the psychiatric consultancy show that motion recorded during a continuous performance test (a forced concentration task) provides a better classification performance than that recorded during "free time".},
keywords = {Attention deficit/hyperactivity disorder, Classification, Inertial sensors, Machine learning, Objective diagnosis},
pubstate = {published},
tppubtype = {article}
}
Read, Jesse; Bielza, Concha; Larranaga, Pedro
Multi-Dimensional Classification with Super-Classes Artículo de revista
En: IEEE Transactions on Knowledge and Data Engineering, vol. 26, no 7, pp. 1720–1733, 2014, ISSN: 1041-4347.
Resumen | Enlaces | BibTeX | Etiquetas: Accuracy, Bayes methods, Classification, COMPRHENSION, conditional dependence, Context, core goals, data instance, evaluation metrics, Integrated circuit modeling, modeling class dependencies, multi-dimensional, Multi-dimensional classification, multidimensional classification problem, multidimensional datasets, multidimensional learners, multilabel classification, multilabel research, multiple class variables, ordinary class, pattern classification, problem transformation, recently-popularized task, super classes, super-class partitions, tractable running time, Training, Vectors
@article{Read2014bb,
title = {Multi-Dimensional Classification with Super-Classes},
author = {Jesse Read and Concha Bielza and Pedro Larranaga},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6648319},
issn = {1041-4347},
year = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Knowledge and Data Engineering},
volume = {26},
number = {7},
pages = {1720--1733},
publisher = {IEEE},
abstract = {The multi-dimensional classification problem is a generalisation of the recently-popularised task of multi-label classification, where each data instance is associated with multiple class variables. There has been relatively little research carried out specific to multi-dimensional classification and, although one of the core goals is similar (modelling dependencies among classes), there are important differences; namely a higher number of possible classifications. In this paper we present method for multi-dimensional classification, drawing from the most relevant multi-label research, and combining it with important novel developments. Using a fast method to model the conditional dependence between class variables, we form super-class partitions and use them to build multi-dimensional learners, learning each super-class as an ordinary class, and thus explicitly modelling class dependencies. Additionally, we present a mechanism to deal with the many class values inherent to super-classes, and thus make learning efficient. To investigate the effectiveness of this approach we carry out an empirical evaluation on a range of multi-dimensional datasets, under different evaluation metrics, and in comparison with high-performing existing multi-dimensional approaches from the literature. Analysis of results shows that our approach offers important performance gains over competing methods, while also exhibiting tractable running time.},
keywords = {Accuracy, Bayes methods, Classification, COMPRHENSION, conditional dependence, Context, core goals, data instance, evaluation metrics, Integrated circuit modeling, modeling class dependencies, multi-dimensional, Multi-dimensional classification, multidimensional classification problem, multidimensional datasets, multidimensional learners, multilabel classification, multilabel research, multiple class variables, ordinary class, pattern classification, problem transformation, recently-popularized task, super classes, super-class partitions, tractable running time, Training, Vectors},
pubstate = {published},
tppubtype = {article}
}