An Interval-based Multiobjective Approach to Feature Subset Selection Using Joint Modeling of Objectives and Variables

This paper studies feature subset selection in classification using a multiobjective estimation of distribution algorithm. We consider six functions, namely area under ROC curve, sensitivity, specificity, precision, F1 measure and Brier score, for evaluation of feature subsets and as the objectives of the problem. One of the characteristics of these objective functions is the existence of noise in their values that should be appropriately handled during optimization. Our proposed algorithm consists of two major techniques which are specially designed for the feature subset selection problem. The first one is a solution ranking method based on interval values to handle the noise in the objectives of this problem. The second one is a model estimation method for learning a joint probabilistic model of objectives and variables which is used to generate new solutions and advance through the search space. To simplify model estimation, l1 regularized regression is used to select a subset of problem variables before model learning. The proposed algorithm is compared with a well-known ranking method for interval-valued objectives and a standard multiobjective genetic algorithm. Particularly, the effects of the two new techniques are experimentally investigated. The experimental results show that the proposed algorithm is able to obtain comparable or better performance on the tested datasets.

Bandlimited Airy Pulses for Invariant Propagation in Single-Mode Fibers

By spectral analysis, and using joint time-frequency representations, we present the theoretical basis to design invariant bandlimited Airy pulses with an arbitrary degree of robustness and an arbitrary range of single-mode fiber chromatic dispersion. The numerically simulated examples confirm the theoretically predicted pulse partial invariance in the propagation of the pulse in the fiber.

Time-Frequency based Feature Selection for Discrimination of non stationary Biosignals.

This research proposes a generic methodology for dimensionality reduction upon time-frequency representations applied to the classification of different types of biosignals. The methodology directly deals with the highly redundant and irrelevant data contained in these representations, combining a first stage of irrelevant data removal by variable selection, with a second stage of redundancy reduction using methods based on linear transformations. The study addresses two techniques that provided a similar performance: the first one is based on the selection of a set of the most relevant time?frequency points, whereas the second one selects the most relevant frequency bands. The first methodology needs a lower quantity of components, leading to a lower feature space; but the second improves the capture of the time-varying dynamics of the signal, and therefore provides a more stable performance. In order to evaluate the generalization capabilities of the methodology proposed it has been applied to two types of biosignals with different kinds of non-stationary behaviors: electroencephalographic and phonocardiographic biosignals. Even when these two databases contain samples with different degrees of complexity and a wide variety of characterizing patterns, the results demonstrate a good accuracy for the detection of pathologies, over 98%.The results open the possibility to extrapolate the methodology to the study of other biosignals.

A new feature extraction method for signal classification applied to cat spinal cord signals

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods.

Feature selection in the reconstruction of complex network representations of spectral data

Complex networks have been extensively used in the last decade to characterize and analyze complex systems, and they have been recently proposed as a novel instrument for the analysis of spectra extracted from biological samples. Yet, the high number of measurements composing spectra, and the consequent high computational cost, make a direct network analysis unfeasible. We here present a comparative analysis of three customary feature selection algorithms, including the binning of spectral data and the use of information theory metrics. Such algorithms are compared by assessing the score obtained in a classification task, where healthy subjects and people suffering from different types of cancers should be discriminated. Results indicate that a feature selection strategy based on Mutual Information outperforms the more classical data binning, while allowing a reduction of the dimensionality of the data set in two orders of magnitude

Simulating improved combinations tillage-rotation under dryland conditions.

The adequate combination of reduced tillage and crop rotation could increase the viability of dry land agriculture in Mediterrenean zones. Crop simulation models can support to examine various tillage-rotation combinations and explore management scenarios. The decision support system for agrotechnology transfer (DSSAT) (Hoogenboom et al., 2010) provides a suite of crop models suitable for this task. The objective of this work was to simulate the effects of two tillage systems, conventional tillage (ConvT) and no tillage (NoT), and three crop rotations, continuous cereal (CC), fallow-cereal (FallowC) and legume-cereal (LegumeC), under dry conditions, on the cereal yield, soil organic carbon (SOC) and nitrogen (SON) in a 15-year experiment, comparing these simulations with field observations.

Mining recurring concepts in a dynamic feature space

Most data stream classification techniques assume that the underlying feature space is static. However, in real-world applications the set of features and their relevance to the target concept may change over time. In addition, when the underlying concepts reappear, reusing previously learnt models can enhance the learning process in terms of accuracy and processing time at the expense of manageable memory consumption. In this paper, we propose mining recurring concepts in a dynamic feature space (MReC-DFS), a data stream classification system to address the challenges of learning recurring concepts in a dynamic feature space while simultaneously reducing the memory cost associated with storing past models. MReC-DFS is able to detect and adapt to concept changes using the performance of the learning process and contextual information. To handle recurring concepts, stored models are combined in a dynamically weighted ensemble. Incremental feature selection is performed to reduce the combined feature space. This contribution allows MReC-DFS to store only the features most relevant to the learnt concepts, which in turn increases the memory efficiency of the technique. In addition, an incremental feature selection method is proposed that dynamically determines the threshold between relevant and irrelevant features. Experimental results demonstrating the high accuracy of MReC-DFS compared with state-of-the-art techniques on a variety of real datasets are presented. The results also show the superior memory efficiency of MReC-DFS.

Posibilidades del motor 3D de la Nintendo DS

La consola portátil Nintendo DS es una plataforma de desarrollo muy presente entre la comunidad de desarrolladores independientes, con una extensa y nutrida escena homebrew. Si bien las capacidades 2D de la consola están muy aprovechadas, dado que la mayor parte de los esfuerzos de los creadores amateur están enfocados en este aspecto, el motor 3D de ésta (el que se encarga de representar en pantalla modelos tridimensionales) no lo está de igual manera. Por lo tanto, en este proyecto se tiene en vista determinar las capacidades gráficas de la Nintendo DS. Para ello se ha realizado una biblioteca de funciones en C que permite aprovechar las posibilidades que ofrece la consola en el terreno 3D y que sirve como herramienta para la comunidad homebrew para crear aplicaciones 3D de forma sencilla, dado que se ha diseñado como un sistema modular y accesible. En cuanto al proceso de renderizado se han sacado varias conclusiones. En primer lugar se ha determinado la posibilidad de asignar varias componentes de color a un mismo vértice (color material reactivo a la iluminación, color por vértice directo y color de textura), tanto de forma independiente como simultáneamente, pudiéndose utilizar para aplicar diversos efectos al modelo, como iluminación pre-calculada o simulación de una textura mediante color por vértice, ahorrando en memoria de video. Por otro lado se ha implementado un sistema de renderizado multi-capa, que permite realizar varias pasadas de render, pudiendo, de esta forma, aplicar al modelo una segunda textura mezclada con la principal o realizar un efecto de reflexión esférica. Uno de los principales avances de esta herramienta con respecto a otras existentes se encuentra en el apartado de animación. El renderizador desarrollado permite por un lado animación por transformación, consistente en la animación de mallas o grupos de vértices del modelo mediante el movimiento de una articulación asociada que determina su posición y rotación en cada frame de animación. Por otro lado se ha implementado un sistema de animación por muestreo de vértices mediante el cual se determina la posición de éstos en cada instante de la animación, generando frame a frame las poses que componen el movimiento (siendo este último método necesario cuando no se puede animar una malla por transformación). Un mismo modelo puede contener diferentes esqueletos, animados independientemente entre sí, y cada uno de ellos tener definidas varias costumbres de animación que correspondan a movimientos contextuales diferentes (andar, correr, saltar, etc). Además, el sistema permite extraer cualquier articulación para asociar su transformación a un objeto estático externo y que éste siga el movimiento de la animación, pudiendo así, por ejemplo, equipar un objeto en la mano de un personaje. Finalmente se han implementado varios efectos útiles en la creación de escenas tridimensionales, como el billboarding (tanto esférico como cilíndrico), que restringe la rotación de un modelo para que éste siempre mire a cámara y así poder emular la apariencia de un objeto tridimensional mediante una imagen plana, ahorrando geometría, o emplearlo para realizar efectos de partículas. Por otra parte se ha implementado un sistema de animación de texturas por subimágenes que permite generar efectos de movimiento mediante imágenes, sin necesidad de transformar geometría. ABSTRACT. The Nintendo DS portable console has received great interest within the independent developers’ community, with a huge homebrew scene. The 2D capabilities of this console are well known and used since most efforts of the amateur creators has been focused on this point. However its 3D engine (which handles with the representation of three-dimensional models) is not equally used. Therefore, in this project the main objective is to assess the Nintendo DS graphic capabilities. For this purpose, a library of functions in C programming language has been coded. This library allows the programmer to take advantage of the possibilities that the 3D area brings. This way the library can be used by the homebrew community as a tool to create 3D applications in an easy way, since it has been designed as a modular and accessible system. Regarding the render process, some conclusions have been drawn. First, it is possible to assign several colour components to the same vertex (material colour, reactive to the illumination, colour per vertex and texture colour), independently and simultaneously. This feature can be useful to apply certain effects on the model, such as pre-calculated illumination or the simulation of a texture using colour per vertex, providing video memory saving. Moreover, a multi-layer render system has been implemented. This system allows the programmer to issue several render passes on the same model. This new feature brings the possibility to apply to the model a second texture blended with the main one or simulate a spherical reflection effect. One of the main advances of this tool over existing ones consists of its animation system. The developed renderer includes, on the one hand, transform animation, which consists on animating a mesh or groups of vertices of the model by the movement of an associated joint. This joint determines position and rotation of the mesh at each frame of the animation. On the other hand, this tool also implements an animation system by vertex sampling, where the position of vertices is determined at every instant of the animation, generating the poses that build up the movement (the latter method is mandatory when a mesh cannot be animated by transform). A model can contain multiple skeletons, animated independently, each of them being defined with several animation customs, corresponding to different contextual movements (walk, run, jump, etc). Besides, the system allows extraction of information from any joint in order to associate its transform to a static external object, which will follow the movement of the animation. This way, any object could be equipped, for example, on the hand of a character. Finally, some useful effects for the creation of three-dimensional scenes have been implemented. These effects include billboarding (both spherical and cylindrical), which constraints the rotation of a model so it always looks on the camera's direction. This feature can provide the ability to emulate the appearance of a three-dimensional model through a flat image (saving geometry). It can also be helpful in the implementation of particle effects. Moreover, a texture animation system using sub-images has also been implemented. This system allows the generation of movement by using images as textures, without having to transform geometry.

Sudden transition to chaos in plasma wave interactions

The coherent three-wave interaction, with linear growth in the higher frequency wave and damping in the two other waves, is reconsidered; for equal dampings, the resulting three-dimensional (3-D) flow of a relative phase and just two amplitudes behaved chaotically, no matter how small the growth of the unstable wave. The general case of different dampings is studied here to test whether, and how, that hard scenario for chaos is preserved in passing from 3-D to four-dimensional flows. It is found that the wave with higher damping is partially slaved to the other damped wave; this retains a feature of the original problem an invariant surface that meets an unstable fixed point, at zero growth rate! that gave rise to the chaotic attractor and determined its structure, and suggests that the sudden transition to chaos should appear in more complex wave interactions.

Multiple Hashing Integration for Real-Time Large Scale Part-to-Part Video Matching

A real-time large scale part-to-part video matching algorithm, based on the cross correlation of the intensity of motion curves, is proposed with a view to originality recognition, video database cleansing, copyright enforcement, video tagging or video result re-ranking. Moreover, it is suggested how the most representative hashes and distance functions - strada, discrete cosine transformation, Marr-Hildreth and radial - should be integrated in order for the matching algorithm to be invariant against blur, compression and rotation distortions: (R; _) 2 [1; 20]_[1; 8], from 512_512 to 32_32pixels2 and from 10 to 180_. The DCT hash is invariant against blur and compression up to 64x64 pixels2. Nevertheless, although its performance against rotation is the best, with a success up to 70%, it should be combined with the Marr-Hildreth distance function. With the latter, the image selected by the DCT hash should be at a distance lower than 1.15 times the Marr-Hildreth minimum distance.

Automatic blood glucose classification for gestational diabetes with feature selection: decision trees vs neural networks

Automatic blood glucose classification may help specialists to provide a better interpretation of blood glucose data, downloaded directly from patients glucose meter and will contribute in the development of decision support systems for gestational diabetes. This paper presents an automatic blood glucose classifier for gestational diabetes that compares 6 different feature selection methods for two machine learning algorithms: neural networks and decision trees. Three searching algorithms, Greedy, Best First and Genetic, were combined with two different evaluators, CSF and Wrapper, for the feature selection. The study has been made with 6080 blood glucose measurements from 25 patients. Decision trees with a feature set selected with the Wrapper evaluator and the Best first search algorithm obtained the best accuracy: 95.92%.

Brain structures identification based on feature descriptor

Traumatic Brain Injury -TBI- -1- is defined as an acute event that causes certain damage to areas of the brain. TBI may result in a significant impairment of an individuals physical, cognitive and psychosocial functioning. The main consequence of TBI is a dramatic change in the individuals daily life involving a profound disruption of the family, a loss of future income capacity and an increase of lifetime cost. One of the main challenges of TBI Neuroimaging is to develop robust automated image analysis methods to detect signatures of TBI, such as: hyper-intensity areas, changes in image contrast and in brain shape. The final goal of this research is to develop a method to identify the altered brain structures by automatically detecting landmarks on the image where signal changes and to provide comprehensive information to the clinician about them. These landmarks identify injured structures by co-registering the patient?s image with an atlas where landmarks have been previously detected. The research work has been initiated by identifying brain structures on healthy subjects to validate the proposed method. Later, this method will be used to identify modified structures on TBI imaging studies.

N2O emissions and N efficiency due to tillage systems and crop rotation under a rainfed Mediterranean agroecosystem

Application of nitrogen (N) fertilizers in agricultural soils increases the risk of N loss to the atmosphere in the form of ammonia (NH3), nitrous oxide (N2O) and nitric oxide (NO)and the water bodies as nitrate (NO3-). The implementation of agricultural management practices can affect these losses. In Mediterranean irrigation systems, the greatest losses of NO3-through leaching occur within the irrigation and the intercropperiod. One way to abate these losses during the intercrop period is the use of cover crops that absorb part of the residual N from the root zone (Gabriel and Quemada, 2011). Moreover, during the following crop, these species could be applied as amendments to the soil, providing both C and N to the soil. This effect of cover and catch crops on decreasing the pool of N potentially lost has focused primarily on NO3-leaching. The aim of this work was to evaluate the effect of cover crops on N2O emission during the in tercrop period in a maize system and its subsequent incorporation into the soil in the following maize crop.

Variable structure control with chattering reduction of a generalized T-S model

In this paper, a fuzzy logic controller (FLC) based variable structure control (VSC) is presented. The main objective is to obtain an improved performance of highly non-linear unstable systems. New functions for chattering reduction and error convergence without sacrificing invariant properties are proposed. The main feature of the proposed method is that the switching function is added as an additional fuzzy variable and will be introduced in the premise part of the fuzzy rules; together with the state variables. In this work, a tuning of the well known weighting parameters approach is proposed to optimize local and global approximation and modelling capability of the Takagi-Sugeno (T-S) fuzzy model to improve the choice of the performance index and minimize it. The main problem encountered is that the T-S identification method can not be applied when the membership functions are overlapped by pairs. This in turn restricts the application of the T-S method because this type of membership function has been widely used in control applications. The approach developed here can be considered as a generalized version of the T-S method. An inverted pendulum mounted on a cart is chosen to evaluate the robustness, effectiveness, accuracy and remarkable performance of the proposed estimation approach in comparison with the original T-S model. Simulation results indicate the potential, simplicity and generality of the estimation method and the robustness of the chattering reduction algorithm. In this paper, we prove that the proposed estimation algorithm converge the very fast, thereby making it very practical to use. The application of the proposed FLC-VSC shows that both alleviation of chattering and robust performance are achieved.