4,067 research outputs found
A survey of cost-sensitive decision tree induction algorithms
The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms including approaches that are direct adaptations of accuracy based methods, use genetic algorithms, use anytime methods and utilize boosting and bagging. The survey brings together these different studies and novel approaches to cost-sensitive decision tree learning, provides a useful taxonomy, a historical timeline of how the field has developed and should provide a useful reference point for future research in this field
Toward Optimal Feature Selection in Naive Bayes for Text Categorization
Automated feature selection is important for text categorization to reduce
the feature size and to speed up the learning process of classifiers. In this
paper, we present a novel and efficient feature selection framework based on
the Information Theory, which aims to rank the features with their
discriminative capacity for classification. We first revisit two information
measures: Kullback-Leibler divergence and Jeffreys divergence for binary
hypothesis testing, and analyze their asymptotic properties relating to type I
and type II errors of a Bayesian classifier. We then introduce a new divergence
measure, called Jeffreys-Multi-Hypothesis (JMH) divergence, to measure
multi-distribution divergence for multi-class classification. Based on the
JMH-divergence, we develop two efficient feature selection methods, termed
maximum discrimination () and methods, for text categorization.
The promising results of extensive experiments demonstrate the effectiveness of
the proposed approaches.Comment: This paper has been submitted to the IEEE Trans. Knowledge and Data
Engineering. 14 pages, 5 figure
Guided Proofreading of Automatic Segmentations for Connectomics
Automatic cell image segmentation methods in connectomics produce merge and
split errors, which require correction through proofreading. Previous research
has identified the visual search for these errors as the bottleneck in
interactive proofreading. To aid error correction, we develop two classifiers
that automatically recommend candidate merges and splits to the user. These
classifiers use a convolutional neural network (CNN) that has been trained with
errors in automatic segmentations against expert-labeled ground truth. Our
classifiers detect potentially-erroneous regions by considering a large context
region around a segmentation boundary. Corrections can then be performed by a
user with yes/no decisions, which reduces variation of information 7.5x faster
than previous proofreading methods. We also present a fully-automatic mode that
uses a probability threshold to make merge/split decisions. Extensive
experiments using the automatic approach and comparing performance of novice
and expert users demonstrate that our method performs favorably against
state-of-the-art proofreading methods on different connectomics datasets.Comment: Supplemental material available at
http://rhoana.org/guidedproofreading/supplemental.pd
Markov modelling on human activity recognition
Human Activity Recognition (HAR) is a research topic with a relevant interest
in the machine learning community. Understanding the activities that a person
is performing and the context where they perform them has a huge importance
in multiple applications, including medical research, security or patient monitoring.
The improvement of the smart-phones and inertial sensors technologies has
lead to the implementation of activity recognition systems based on these devices,
either by themselves or combining their information with other sensors. Since
humans perform their daily activities sequentially in a specific order, there exist
some temporal information in the physical activities that characterize the different
human behaviour patterns. However, the most popular approach in HAR is to assume
that the data is conditionally independent, segmenting the data in different
windows and extracting the most relevant features from each segment.
In this thesis we employ the temporal information explicitly, where the raw data
provided by the wearable sensors is fed to the training models. Thus, we study
how to perform a Markov modelling implementation of a long-term monitoring
HAR system with wearable sensors, and we address the existing open problems
arising while processing and training the data, combining different sensors and
performing the long-term monitoring with battery powered devices.
Employing directly the signals from the sensors to perform the recognition can
lead to problems due to misplacements of the sensors on the body. We propose an
orientation correction algorithm based on quaternions to process the signals and
find a common frame reference for all of them independently on the position of the
sensors or their orientation. This algorithm allows for a better activity recognition
when feed to the classification algorithm when compared with similar approaches,
and the quaternion transformations allow for a faster implementation.
One of the most popular algorithms to model time series data are Hidden
Markov Models (HMMs) and the training of the parameters of the model is performed
using the Baum-Welch algorithm. However, this algorithm converges to
local maxima and the multiple initializations needed to avoid them makes it computationally expensive for large datasets. We propose employing the theory of
spectral learning to develop a discriminative HMM that avoids the problems of
the Baum-Welch algorithm, outperforming it in both complexity and computational
cost.
When we implement a HAR system with several sensors, we need to consider
how to perform the combination of the information provided by them. Data fusion
can be performed either at signal level or at classification level. When performed
at classification level, the usual approach is to combine the decisions of multiple
classifiers on the body to obtain the performed activities. However, in the simple
case with two classifiers, which can be a practical implementation of a HAR
system, the combination reduces to selecting the most discriminative sensor, and
no performance improvement is obtained against the single sensor implementation.
In this thesis, we propose to employ the soft-outputs of the classifiers in
the combination and we develop a method that considers the Markovian structure
of the ground truth to capture the dynamics of the activities. We will show
that this method improves the recognition of the activities with respect to other
combination methods and with respect to the signal fusion case.
Finally, in long-term monitoring HAR systems with wearable sensors we need
to address the energy efficiency problem that is inherent to battery powered devices.
The most common approach to improve the energy efficiency of such devices
is to reduce the amount of data acquired by the wearable sensors. In that sense,
we introduce a general framework for the energy efficiency of a system with multiple
sensors under several energy restrictions. We propose a sensing strategy to
optimize the temporal data acquisition based on computing the uncertainty of
the activities given the data and adapt the acquisition actively. Furthermore, we
develop a sensor selection algorithm based on Bayesian Experimental Design to
obtain the best configuration of sensors that performs the activity recognition accurately, allowing for a further improvement on the energy efficiency by limiting
the number of sensors employed in the acquisition.El reconocimiento de actividades humanas (HAR) es un tema de investigación
con una gran relevancia para la comunidad de aprendizaje máquina. Comprender
las actividades que una persona está realizando y el contexto en el que las
realiza es de gran importancia en multitud de aplicaciones, entre las que se incluyen
investigación médica, seguridad o monitorización de pacientes. La mejora
en los smart-phones y en las tecnologías de sensores inerciales han dado lugar a
la implementación de sistemas de reconocimiento de actividades basado en dichos
dispositivos, ya sea por si mismos o combinándolos con otro tipo de sensores. Ya
que los seres humanos realizan sus actividades diarias de manera secuencial en un
orden específico, existe una cierta información temporal en las actividades físicas
que caracterizan los diferentes patrones de comportamiento, Sin embargo, los algoritmos
más comunes asumen que los datos son condicionalmente independientes,
segmentándolos en diferentes ventanas y extrayendo las características más relevantes
de cada segmento.
En esta tesis utilizamos la información temporal de manera explícita, usando
los datos crudos de los sensores como entrada de los modelos de entrenamiento. Por
ello, analizamos como implementar modelos Markovianos para el reconocimiento
de actividades en monitorizaciones de larga duración con sensores wearable, y
tratamos los problemas existentes al procesar y entrenar los datos, al combinar
diferentes sensores y al realizar adquisiciones de larga duración con dispositivos
alimentados por baterías.
Emplear directamente las señales de los sensores para realizar el reconocimiento
de actividades puede dar lugar a problemas debido a la incorrecta colocación de
los sensores en el cuerpo. Proponemos un algoritmo de corrección de la orientación
basado en quaterniones para procesar las señales y encontrar un marco de referencia
común independiente de la posición de los sensores y su orientación. Este
algoritmo permite obtener un mejor reconocimiento de actividades al emplearlo
en conjunto con un algoritmo de clasificación, cuando se compara con modelos similares. Además, la transformación de la orientación basada en quaterniones da
lugar a una implementación más rápida.
Uno de los algoritmos más populares para modelar series temporales son los
modelos ocultos de Markov, donde los parámetros del modelo se entrenan usando
el algoritmo de Baum-Welch. Sin embargo, este algoritmo converge en general
a máximos locales, y las múltiples inicializaciones que se necesitan en su implementación lo convierten en un algoritmo de gran carga computacional cuando se
emplea con bases de datos de un volumen considerable. Proponemos emplear la
teoría de aprendizaje espectral para desarrollar un HMM discriminativo que evita
los problemas del algoritmo de Baum-Welch, superándolo tanto en complejidad
como en coste computacional. Cuando se implementa un sistema de reconocimiento de actividades con múltiples
sensores, necesitamos considerar cómo realizar la combinación de la información que proporcionan. La fusión de los datos, se puede realizar tanto a nivel
de señal como a nivel de clasificación. Cuando se realiza a nivel de clasificación, lo
normal es combinar las decisiones de múltiples clasificadores colocados en el cuerpo
para obtener las actividades que se están realizando. Sin embargo, en un caso simple
donde únicamente se emplean dos sensores, que podría ser una implantación
habitual de un sistema de reconocimiento de actividades, la combinación se reduce
a seleccionar el sensor más discriminativo, y no se obtiene mejora con respecto a
emplear un único sensor. En esta tesis proponemos emplear salidas blandas de
los clasificadores para la combinación, desarrollando un modelo que considera la
estructura Markoviana de los datos reales para capturar la dinámica de las actividades.
Mostraremos como este método mejora el reconocimiento de actividades
con respecto a otros métodos de combinación de clasificadores y con respecto a la
fusión de los datos a nivel de señal.
Por último, abordamos el problema de la eficiencia energética de dispositivos
alimentados por baterías en sistemas de reconocimiento de actividades de larga
duración. La aproximación más habitual para mejorar la eficiencia energética consiste
en reducir el volumen de datos que adquieren los sensores. En ese sentido, introducimos un marco general para tratar el problema de la eficiencia energética
en un sistema con múltiples sensores bajo ciertas restricciones de energética. Proponemos
una estrategia de adquisición activa para optimizar el sistema temporal
de recogida de datos, basándonos en la incertidumbre de las actividades dados los
datos que conocemos. Además, desarrollamos un algoritmo de selección de sensores
basado diseño experimental Bayesiano y así obtener la mejor configuración
para realizar el reconocimiento de actividades limitando el número de sensores
empleados y al mismo tiempo reduciendo su consumo energético.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Luis Ignacio Santamaría Caballero.- Secretario: Pablo Martínez Olmos.- Vocal: Alberto Suárez Gonzále
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