Search results for "Feature selection"

Estrategias para la elaboración de modelos estadísticos de regresión

2011

Multivariable regression models are widely used in health science research, mainly for two purposes: prediction and effect estimation. Various strategies have been recommended when building a regression model: a) use the right statistical method that matches the structure of the data; b) ensure an appropriate sample size by limiting the number of variables according to the number of events; c) prevent or correct for model overfitting; d) be aware of the problems associated with automatic variable selection procedures (such as stepwise), and e) always assess the performance of the final model in regard to calibration and discrimination measures. If resources allow, validate the prediction mo…

Designing a framework for assisting depression severity assessment from facial image analysis

2015

Depression is one of the most common mental disorders affecting millions of people worldwide. Developing adjunct tools aiding depression assessment is expected to impact overall health outcomes and treatment cost reduction. To this end, platforms designed for automatic and non-invasive depression assessment could help in detecting signs of the disease on a regular basis, without requiring the physical presence of a mental health professional. Despite the different approaches that can be found in the literature, both in terms of methods and algorithms, a fully satisfactory system for the automatic assessment of depression severity has not been presented as yet. This paper describes a propose…

Video-based Pain Level Assessment: Feature Selection and Inter-Subject Variability Modeling

2018

Automatic pain level assessment, based on video features, may provide clinically-relevant, objective measures of pain intensity. In various clinical contexts accurate pain level estimation by health care personnel is challenging. This problem is compounded by considerable inter- and intra-individual variability of both perceived pain levels and of the associated facial expressions, especially at low pain levels. Thus, providing objective video-based indices for pain level assessment is a rather computationally challenging problem. In the present work both geometric and color-based features were extracted. The most informative features were identified with lasso regression, and subject varia…

Robust Conditional Independence maps of single-voxel Magnetic Resonance Spectra to elucidate associations between brain tumours and metabolites.

2020

The aim of the paper is two-fold. First, we show that structure finding with the PC algorithm can be inherently unstable and requires further operational constraints in order to consistently obtain models that are faithful to the data. We propose a methodology to stabilise the structure finding process, minimising both false positive and false negative error rates. This is demonstrated with synthetic data. Second, to apply the proposed structure finding methodology to a data set comprising single-voxel Magnetic Resonance Spectra of normal brain and three classes of brain tumours, to elucidate the associations between brain tumour types and a range of observed metabolites that are known to b…

2020

The analysis of tumours using biomarkers in blood is transforming cancer diagnosis and therapy. Cancers are characterised by evolving genetic alterations, making it difficult to develop reliable and broadly applicable DNA-based biomarkers for liquid biopsy. In contrast to the variability in gene mutations, the methylation pattern remains generally constant during carcinogenesis. Thus, methylation more than mutation analysis may be exploited to recognise tumour features in the blood of patients. In this work, we investigated the possibility of using global CpG (CpG means a CG motif in the context of methylation. The p represents the phosphate. This is used to distinguish CG sites meant for m…

Early detection of mechanical damage in mango using NIR hyperspectral images and machine learning

2014

Mango fruit are sensitive and can easily develop brown spots after suffering mechanical stress during postharvest handling, transport and marketing. The manual inspection of this fruit used today cannot detect the damage in very early stages of maturity and to date no automatic tool capable of such detection has been developed, since current systems based on machine vision only detect very visible damage. The application of hyperspectral imaging to the postharvest quality inspection of fruit is relatively recent and research is still underway to find a method of estimating internal properties or detecting invisible damage. This work describes a new system to evaluate mechanically induced da…

Using the dglars Package to Estimate a Sparse Generalized Linear Model

2015

dglars is a publicly available R package that implements the method proposed in Augugliaro et al. (J. R. Statist. Soc. B 75(3), 471-498, 2013) developed to study the sparse structure of a generalized linear model (GLM). This method, called dgLARS, is based on a differential geometrical extension of the least angle regression method. The core of the dglars package consists of two algorithms implemented in Fortran 90 to efficiently compute the solution curve. dglars is a publicly available R package that implements the method proposed in Augugliaro et al. (J. R. Statist. Soc. B 75(3), 471-498, 2013) developed to study the sparse structure of a generalized linear model (GLM). This method, call…

Hyperspectral LCTF-based system for classification of decay in mandarins caused by Penicillium digitatum and Penicillium italicum using the most rele…

2013

[EN] Green mold (Penicillium digitatum) and blue mold (Penicillium italicum) are important sources of postharvest decay affecting the commercialization of mandarins. These fungi infections produce enormous economic losses in mandarin production if early detection is not carried out. Nowadays, this detection is performed manually in dark chambers, where the fruit is illuminated by ultraviolet light to produce fluorescence, which is potentially dangerous for humans. This paper documents a new methodology based on hyperspectral imaging and advanced machine-learning techniques (artificial neural networks and classification and regression trees) for the segmentation and classification of images …

Optimization of image parameters using a hyperspectral library application to soil identification and moisture estimation

2009

The growing number of sensors raises questions about the image parameters required for the application, soil identification and moisture estimation. Hyperspectral images are also known to contain highly redundant information. Hence not all the spectral bands are needed for the satisfactory classification of the soil types. Hence, the work was aimed at obtaining these optimal spectral bands for identifying the soil types and to use these spectral bands to estimate the moisture content of the soils using the method proposed by Whiting et.al.

Estimating feature discriminant power in decision tree classifiers

1995

Feature Selection is an important phase in pattern recognition system design. Even though there are well established algorithms that are generally applicable, the requirement of using certain type of criteria for some practical problems makes most of the resulting methods highly inefficient. In this work, a method is proposed to rank a given set of features in the particular case of Decision Tree classifiers, using the same information generated while constructing the tree. The preliminary results obtained with both synthetic and real data confirm that the performance is comparable to that of sequential methods with much less computation.