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Social media allow web surfers to produce and share content about different subjects, exposing their activities, opinions, feelings and thoughts. In this context, online social media has attracted the interest of data analysis researchers seeking to infer behaviors and trends, besides creating statistics involving social sites. A possible research involving these data is known as personality analysis,...
Electronic commerce (e-commerce) has grown rapidly over the past years. Products, services and information of different types are offered daily for many Internet users. Finding out an appropriate strategy to offer a product to each customer in a personalized fashion is the goal of a recommender system. This association between items is a task that falls under the umbrella of data mining, more specifically...
Finding Nash equilibria has been one of the early objectives of research in game theory, and still represents a challenge to this day. We introduce a multiobjective formulation for computing Pareto-optimal sets of mixed Nash equilibria in normal form games. Computing these sets can be notably useful in decision making, because it focuses the analysis on solutions with greater outcome and hence more...
This paper introduces two new approaches to fit univariate resistant linear regression models on interval-valued data. Linear regressions on interval-valued data gives point predictions. The prediction of the lower and upper bounds from interval-valued data of dependent variable are estimated from the fitted range resistant linear regression model. The new proposed methods should be used in presence...
This work is the application of a Multilayer Perceptron Artificial Neural Network (MLP ANN) to detect early interturn short-circuit faults in a three-phase converter-fed induction motor. The quantity used to analyze the problem is the stator current or, more specifically, the harmonic content of its frequency spectrum, also called current signature. The analysis through the current signature is a...
Unsupervised models can provide supplementary soft constraints to help classify new data since similar instances are more likely to share the same class label. In this context, we investigate how to make an existing algorithm, named C3E (from Combining Classifier and Cluster Ensembles), more user-friendly by automatically tunning its main parameters with the use of metaheuristics. In particular, the...
Estimation of Distribution Algorithms (EDA) are stochastic population based search algorithms that use a distribution model of the population to create new candidate solutions. One problem that directly affects the EDAs' ability to find the best solutions is the premature convergence to some local optimum due to diversity loss. Inspired by the Random Immigrants technique, this paper presents the Bayesian...
Despite being extensively used in many fields of science, there is still no general procedure to determine the most suitable neural architecture for a given task. Even today the design of an artificial neural network (ANN) is still very dependent on the designer's experience. Several methods for automatic design of ANNs have been proposed, but they are not biologically inspired, in this paper it's...
Mapping and classification of human settlements from remotely sensed data has attracted a lot of attention in recent years. Real world data, however, often suffer from corruptions or noise but not always known. This is the heart of information-based remote sensing models. This paper investigates the impact of incomplete remotely sensed data in the evaluation of machine learning techniques (classifiers)...
This paper presents a new validity index for fuzzy partitions generated by the fuzzy c-means algorithm. The proposed validity index is based on the calculation of factors from the proximity matrix generated from the membership matrix generated by a fuzzy clustering partition algorithm, such as FCM. The experimental results show that the proposed approach is consistent with other well-known metrics...
Automatic object recognition in digital satellite images is not a simple task due to several variations present in the capture process and object appearance and pose, consequently, different general purpose techniques have been proposed. In this paper, an approach with LBP boosted cascade classifier for automatic runway detection in high resolution satellite imagery is analyzed. Promising results...
In this paper the visual odometry and the localization of moving objects from aerial images are addressed. The techniques used in this work are the Oriented FAST and Rotated BRIEF (ORB) descriptor to detect and extract the interest points and the Random Sample Consensus (RANSAC) method to estimate the parameters from a matched points matrix for finding the camera translation. The visual odometry and...
Semi-supervised learning algorithms address the problem of learning from partially labeled data. However, most of the semi-supervised classification methods proposed in the literature considers a stationary distribution of data. Which means that future data patterns tend to conform to the data distribution presented in data set throughout the application lifetime. However, for plenty of new variety...
Optical fibers are commonly used in communications today, mainly because that the data transmission rates of those systems are faster than those in any other digital communication system. Despite this great advantage, some problems prevent the full use of optical connection: by increasing transmission rates over longer distances, the data is affected by non-linear inter-symbol interference caused...
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