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Nearest prototype methods are a successful trend of many pattern classification tasks. However, they present several shortcomings such as time response, noise sensitivity, and storage requirements. Data reduction techniques are suitable to alleviate these drawbacks. Prototype generation is an appropriate process for data reduction, which allows the fitting of a dataset for nearest neighbor (NN) classification. This brief presents a methodology to learn iteratively the positioning of prototypes using real parameter optimization procedures. Concretely, we propose an iterative prototype adjustment technique based on differential evolution. The results obtained are contrasted with nonparametric statistical tests and show that our proposal consistently outperforms previously proposed methods, thus becoming a suitable tool in the task of enhancing the performance of the NN classifier. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the experimental analysis on the performance in artificial neural networks with the use of statistical tests on the classification task. Particularly, we have studied whether the sample of results from multiple trials obtained by conventional artificial neural networks and support vector machines checks the necessary conditions for being analyzed through parametrical tests. The study is conducted by considering three possibilities on classification experiments: random variation in the selection of test data, the selection of training data and internal randomness in the learning algorithm. The results obtained state that the fulfillment of these conditions are problem-dependent and indefinite, which justifies the need of using non-parametric statistics in the experimental analysis. [Copyright &y& Elsevier]

Sentiment analysis requires a lot of information coming from different sources and about different topics to be retrieved and fused. For this reason, one of the most important subtasks of sentiment analysis is subjectivity detection, i.e., the removal of ‘factual’ or ‘neutral’ comments that lack sentiment. It is possibly the most essential subtask of sentiment analysis as sentiment classifiers are often optimized to categorize text as either negative or positive and, hence, forcefully fit unopinionated sentences into one of these two categories. This article reviews hand-crafted and automatic models for subjectivity detection in the literature. It highlights the key assumptions these models make, the results they obtain, and the issues that still need to be explored to further our understanding of subjective sentences. Lastly, the advantages and limitations of each approach are compared. The methods can be broadly categorized as hand-crafted, automatic, and multi-modal. Hand-crafted templates work well on strong sentiments, however they are unable to identify weakly subjective sentences. Automatic methods such as deep learning provide a meta-level feature representation that generalizes well on new domains and languages. Multi-modal methods can combine the abundant audio and video forms of social data with text using multiple kernels. We conclude that the high-dimensionality of n -gram features and temporal nature of sentiments in long product reviews are the major challenges in sentiment mining from text. [ABSTRACT FROM AUTHOR]

Modern machine learning algorithms excel in a great variety of tasks, but at the same time, it is also known that those complex models need to deal with uncertainty from different sources. Consequently, understanding if the model is indeed making accurate predictions or simply guessing at random is not trivial, and measuring the confidence bounds becomes very important. Bayesian machine learning seems to provide the solution, however, many of the state-of-the-art Bayesian algorithms use rigid parametric representations of the uncertainty where the learning process depends on the gradient of a predefined cost function. In this article, a new gradient-free training algorithm based on Approximate Bayesian Computation by Subset Simulation is proposed, where the likelihood function and the weights are defined by non-parametric formulations, resulting in a flexible and fairer representation of the uncertainty. The experiments, specially the engineering case study on composite materials subject to fatigue damage, show the ability of the proposed algorithm to consistently reach accurate predictions while avoiding gradient related instabilities, and most importantly, it provides a realistic and coherent quantification of the uncertainty represented by confidence bounds. All this may lead to a reduction of safety factors in engineering problems, and in general, allows us to make well-informed decisions in situations with a high degree of uncertainty and risk. A comparison with the state-of-the-art Bayesian Neural Networks is also carried out. • Neural networks trained with approximate Bayesian computation. • Accurate and flexible representation of the uncertainty in the observed data. • Stability of predictions thanks to the gradient-free nature of the algorithm. • Non-parametric weights and likelihood function provide adaptability to data. • Appropriate when decisions are dependent on the level of uncertainty. [ABSTRACT FROM AUTHOR]

Accurate tree cover mapping is of paramount importance in many fields, from biodiversity conservation to carbon stock estimation, ecohydrology, erosion control, or Earth system modelling. Despite this importance, there is still uncertainty about global forest cover, particularly in drylands. Recently, the Food and Agriculture Organization of the United Nations (FAO) conducted a costly global assessment of dryland forest cover through the visual interpretation of orthoimages using the Collect Earth software, involving hundreds of operators from around the world. Our study proposes a new automatic method for estimating tree cover using artificial intelligence and free orthoimages. Our results show that our tree cover classification model, based on convolutional neural networks (CNN), is 23% more accurate than the manual visual interpretation used by FAO, reaching up to 79% overall accuracy. The smallest differences between the two methods occurred in the driest regions, but disagreement increased with the percentage of tree cover. The application of CNNs could be used to improve and reduce the cost of tree cover maps from the local to the global scale, with broad implications for research and management. [ABSTRACT FROM AUTHOR]