Browsing by Subject "Support vector machines"

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    Analysis of the hyperparameter optimisation of four machine learning satellite imagery classification methods
    (Springer, 2024-04-05) Alonso Sarría, Francisco; Valdivieso Ros, Carmen; Gomariz Castillo, Francisco; Geografía
    The classification of land use and land cover (LULC) from remotely sensed imagery in semi-arid Mediterranean areas is a challenging task due to the fragmentation of the landscape and the diversity of spatial patterns. Recently, the use of deep learning (DL) for image analysis has increased compared to commonly used machine learning (ML) methods. This paper compares the performance of four algorithms, Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Convolutional Network (CNN), using multi-source data, applying an exhaustive optimisation process of the hyperparameters. The usual approach in the optimisation process of a LULC classification model is to keep the best model in terms of accuracy without analysing the rest of the results. In this study, we have analysed such results, discovering noteworthy patterns in a space defined by the mean and standard deviation of the validation accuracy estimated in a 10-fold cross validation (CV). The point distributions in such a space do not appear to be completely random, but show clusters of points that facilitate the discovery of hyperparameter values that tend to increase the mean accuracy and decrease its standard deviation. RF is not the most accurate model, but it is the less sensitive to changes in hyperparameters. Neural Networks, tend to increase commission and omission errors of the less represented classes because their optimisation lead the model to learn better the most frequent classes. On the other hand, RF and MLP prediction layers are the most accurate from a general qualitative point of view.
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    Open Access
    Effect of the Synergetic Use of Sentinel-1, Sentinel-2, LiDAR and Derived Data in Land Cover Classification of a Semiarid Mediterranean Area Using Machine Learning Algorithms
    (Multidisciplinary Digital Publishing Institute, 2023-01-05) Valdivieso Ros, Carmen; Alonso Sarria, Francisco; Gomariz Castillo, Francisco; Geografía
    Land cover classification in semiarid areas is a difficult task that has been tackled using different strategies, such as the use of normalized indices, texture metrics, and the combination of images from different dates or different sensors. In this paper we present the results of an experiment using three sensors (Sentinel-1 SAR, Sentinel-2 MSI and LiDAR), four dates and different normalized indices and texture metrics to classify a semiarid area. Three machine learning algorithms were used: Random Forest, Support Vector Machines and Multilayer Perceptron; Maximum Likelihood was used as a baseline classifier. The synergetic use of all these sources resulted in a significant increase in accuracy, Random Forest being the model reaching the highest accuracy. However, the large amount of features (126) advises the use of feature selection to reduce this figure. After using Variance Inflation Factor and Random Forest feature importance, the amount of features was reduced to 62. The final overall accuracy obtained was 0.91 & PLUSMN; 0.005 (alpha = 0.05) and kappa index 0.898 & PLUSMN; 0.006 (alpha = 0.05). Most of the observed confusions are easily explicable and do not represent a significant difference in agronomic terms.
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    Open Access
    Fall risk detection mechanism in the elderly, based on electromyographic signals, through the use of artificial intelligence
    (Universidad de Murcia, Servicio de Publicaciones, 2023) Arias-Poblete, Leónidas; Álvarez‐Arangua, Sebastián; Jerez-Mayorga, Daniel; Chamorro, Claudio; Ferrero‐Hernández, Paloma; Ferrari, Gerson; Farías‐Valenzuela, Claudio
    Introduction: The tests used to classify older adults at risk of falls are questioned in literature. Tools from the field of artificial intelligence are an alternative to classify older adults more precisely. Objective: To identify the risk of falls in the elderly through electromyographic signals of the lower limb, using tools from the field of artificial intelligence. Methods: A descriptive study design was used. The unit of analysis was made up of 32 older adults (16 with and 16 without risk of falls). The electrical activity of the lower limb muscles was recorded during the functional walking gesture. The cycles obtained were divided into training and validation sets, and then from the amplitude variable, select attributes using the Weka software. Finally, the Support Vector Machines (SVM) classifier was implemented. Results: A classifier of two classes (elderly adults with and without risk of falls) based on SVM was built, whose performance was: Kappa index 0.97 (almost perfect agreement strength), sensitivity 97%, specificity 100%. Conclusions: The SVM artificial intelligence technique applied to the analysis of lower limb electromyographic signals during walking can be considered a precision tool of diagnostic, monitoring and follow-up for older adults with and without risk of falls.