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Publicações

Publicações por Kelwin Alexander Correia

2016

Discriminative directional classifiers

Autores
Fernandes, K; Cardoso, JS;

Publicação
NEUROCOMPUTING

Abstract
In different areas of knowledge, phenomena are represented by directional-angular or periodic-data; from wind direction and geographical coordinates to time references like days of the week or months of the calendar. These values are usually represented in a linear scale, and restricted to a given range (e.g. [0,2 pi)), hiding the real nature of this information. Therefore, dealing with directional data requires special methods. So far, the design of classifiers for periodic variables adopts a generative approach based on the usage of the von Mises distribution or variants. Since for non-periodic variables state of the art approaches are based on non-generative methods, it is pertinent to investigate the suitability of other approaches for periodic variables. We propose a discriminative Directional Logistic Regression model able to deal with angular data, which does not make any assumption on the data distribution. Also, we study the expressiveness of this model for any number of features. Finally, we validate our model against the previously proposed directional naive Bayes approach and against a Support Vector Machine with a directional Radial Basis Function kernel with synthetic and real data obtaining competitive results.

2017

Combining ranking with traditional methods for ordinal class imbalance

Autores
Cruz, R; Fernandes, K; Pinto Costa, JFP; Perez Ortiz, MP; Cardoso, JS;

Publicação
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
In classification problems, a dataset is said to be imbalanced when the distribution of the target variable is very unequal. Classes contribute unequally to the decision boundary, and special metrics are used to evaluate these datasets. In previous work, we presented pairwise ranking as a method for binary imbalanced classification, and extended to the ordinal case using weights. In this work, we extend ordinal classification using traditional balancing methods. A comparison is made against traditional and ordinal SVMs, in which the ranking adaption proposed is found to be competitive. © Springer International Publishing AG 2017.

2014

Pavement pathologies classification using graph-based features

Autores
Fernandes, K; Ciobanu, L;

Publicação
2014 IEEE International Conference on Image Processing, ICIP 2014

Abstract
Pavement cracks involve important information to measure road quality. Crack classification is a challenging problem given the diversity of possible cracks, therefore, it is needed to retrieve good features in order to facilitate the learning of predictive models with as few samples as possible. In this paper, we propose a graph-based set of features to efficiently describe cracks. These features proved to have high degree of expressiveness and robustness when used for crack classification. We show that the proposed features succeed in the assessment of 525 images with different kinds of cracks. We proved the robustness of the approach applying different levels of noise to the images and evaluating the classification accuracy. © 2014 IEEE.

2016

Tackling Class Imbalance with Ranking

Autores
Cruz, R; Fernandes, K; Cardoso, JS; Pinto Costa, JFP;

Publicação
2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)

Abstract
In classification, when there is a disproportion in the number of observations in each class, the data is said to be class imbalance. Class imbalance is pervasive in real world applications of data classification and has been the focus of much research. The minority class contributes too little to the decision boundary because the learning process learns from each observation in isolation. In this paper, we discuss the application of learning pairwise rankers as a solution to class imbalance. We compare ranking models to alternatives from the literature.

2014

Catalogue-Based Traffic Sign Asset Management: Towards User's Effort Minimisation

Autores
Fernandes, K; Silva, PFB; Ciobanu, L; Fonseca, P;

Publicação
IMAGE ANALYSIS AND RECOGNITION, ICIAR 2014, PT I

Abstract
Automatic traffic sign recognition is a difficult task, as it is necessary to distinguish between a very high number of classes with low inter-class variability. The state-of-the-art methods report very high accuracy rates but just a few classes are covered and several training samples are required. For the sake of the development of an asset management system, these approaches are out of reach. Furthermore, in this context, minimizing user's effort is more important than achieving maximal classification accuracy. In this paper, we propose a catalogue-based traffic sign classifier which doesn't require real training samples for model building and promotes minimal user's workload involving the catalogue's semantic structure in the error propagation. Experimental results reveal that user's workload was reduced by 20% while accuracy was improved by 2%.

2015

Temporal Segmentation of Digital Colposcopies

Autores
Fernandes, K; Cardoso, JS; Fernandes, J;

Publicação
PATTERN RECOGNITION AND IMAGE ANALYSIS (IBPRIA 2015)

Abstract
Cervical cancer remains a significant cause of mortality in low-income countries. Digital colposcopy is a promising and inexpensive technology for the detection of cervical intraepithelial neoplasia. However, diagnostic sensitivity varies widely depending on the doctor expertise. Therefore, automation of this process is needed in both, detection and visualization. Colposcopies cover four steps: macroscopic view with magnifier white light, observation under green light, Hinselmann and Schiller. Also, there are transition intervals where the specialist manipulates the observed area. In this paper, we focus on the temporal segmentation of the video in these steps. Using our solution, physicians may focus on the step of interest and lesion detection tools can determine the interval to diagnose. We solved the temporal segmentation problem using Weighted Automata. Images were described by their chromacity histograms and labeled using a KNN classifier with a precision of 97%. Transition frames were recognized with a precision of 91 %.

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