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Applications of evolutionary computation (EC) techniques to computer vision are drawing increasing interest from researchers. There are many different ways in which EC techniques can be used as an effective aid to solve problems in the computer vision domain. This paper provides a brief review of the opportunities offered by EC to researchers in computer vision, trying to classify them according to...
This paper presents a new model to realize a supervised image segmentation task. It is based on the concept of receptive fields that intends to analyze pieces of an image considering not only the pixels or group of them, but also the relationship between them and their neighbors, called segmentation and classification with receptive fields (SCRF). Also, in order to work with the SCRF model, is proposed...
Online adaptation is a key requirement for image processing applications when used in dynamic environments. In contrast to batch learning, where retraining is required each time a new observation occurs, sequential learning algorithms offer the ability to iteratively adapt the existing classifier. In this paper, we present a neural network architecture and a fast online learning algorithm that allow...
Hybrid models combine different technologies to obtain a product that shares their advantages and minimizes their deficiencies. The solutions given by a case-based system (CBS) rely on similar past experiences, which are commonly described in terms of both symbolic and continuous attributes. The nearest neighbor (NN) principle commonly followed to develop CBS for classification task proceeds from...
This paper presents a hybrid optimization method based on the fusion of the clonal selection algorithm (CSA) and harmony search (HS) technique. The CSA is employed to improve the members of the harmony memory in the HS method. The hybrid optimization algorithm is further used to optimize a fuzzy classification system for the Fisher Iris data classification. Computer simulations results demonstrate...
Ensemble methods like bagging combine the decisions of multiple classifiers in order to obtain more accuracy than a single classifier. This paper studies the use of bagging for a region oriented symbolic classifier. Experiments with two artificial data sets, generated according to bi-variate normal distributions have been performed in order to show the usefulness of bagging for this symbolic classifier...
The class imbalance problem (when one of the classes has much less samples than the others) is of great importance in machine learning, because it corresponds to many critical applications. In this work we introduce the recursive partitioning of the majority class (REPMAC) algorithm, a new hybrid method to solve imbalanced problems. Using a clustering method, REPMAC recursively splits the majority...
This paper introduces a weighted partitioning dynamic clustering algorithm for quantitative feature data based on adaptive euclidean distances. The proposed method is an iterative four-steps relocation algorithm involving the determination of the clusters representatives (prototypes), the weight of each individual, the distance associated to each cluster and the construction of the clusters, at each...
Information extraction (IE) aims to extract from textual documents only the fragments which correspond to datafields required by the user. In this paper, we present new experiments evaluating a hybrid machine learning approach for IE that combines text classifiers and hidden Markov models (HMM). In this approach, a text classifier technique generates an initial output, which is refined by an HMM,...
In this paper, we study a single objective extension of support vector machines for multicategory classification. Extending the dual formulation of binary SVMs, the algorithm looks for minimizing the sum of all the pairwise distances among a set of prototypes, each one constrained to one of the convex-hulls enclosing a class of examples. The final discriminant system is built looking for an appropriate...
Machines learning techniques have been applied in several different problems in bioinformatics. Similarly, pattern discovery algorithms have also been used to uncover hidden motifs in protein sequences, contributing greatly to the understanding of the problem of protein classification. G-protein coupled receptors (GPCRs) represent one of the largest protein families in Human Genome. Most of these...
Machine Learning techniques have been largely applied to the problem of class prediction in microarray data. Nevertheless, current approaches to select appropriate methods for such task often result unsatisfactory in many ways, instigating the need for the development of tools to automate the process. In this context, the authors introduce the use of metalearning in the specific domain of gene expression...
Multi-objective meta-heuristics permit to conceive a complete novel approach to induce classifiers, where the properties of the rules can be expressed in different objectives, and then the algorithm finds these rules in an unique run by exploring Pareto dominance concepts. Furthermore, these rules can be used as an unordered classifier, in this way, the rules are more intuitive and easier to understand...
Multi-dimensional classification is a generalization of supervised classification that considers more than one class variable to classify. In this paper we review the existing multi-dimensional Bayesian classifiers and introduce a new one: the KDB multi-dimensional classifier. Then we define different classification rules for multi-dimensional scope. Finally, we introduce a structural learning approach...
In this paper, we highlight the use of synthetic data sets to analyze learners behavior under bounded complexity. We propose a method to generate synthetic data sets with a specific complexity, based on the length of the class boundary. We design a genetic algorithm as a search technique and find it useful to obtain class labels according to the desired complexity. The results show the suitability...
This paper introduces a multiobjective grammar based genetic programming algorithm to solve a Web Mining problem from multiple instance perspective. This algorithm, called MOG3P-MI, is evaluated and compared with other available algorithms which extend a well-known neighborhood-based algorithm (k-nearest neighbour algorithm) and with a mono objective version of grammar guided genetic programming G3P-MI...
Radial basis function networks (RBFNs) have shown their capability to be used in classification problems, so that many data mining algorithms have been developed to configure RBFNs. These algorithms need to be given a suitable set of parameters for every problem they face, thus methods to automatically search the values of these parameters are required. This paper shows the robustness of a meta-algorithm...
For a given data set, different learning algorithms typically provide different classifiers. Although it is possible to simply select the most successful classifier, the less successful classifiers could have potentially valuable information that may be wasted. This work proposes GAESC, an algorithm for evolving a set of classifiers into a single symbolic classifier using genetic algorithms. Individuals...
This paper presents an efficient distributed genetic algorithm for classification rules extraction in data mining, which is based on a new method of dynamic data distribution applied to parallelism using networks of computers in order to mine large datasets. The presented algorithm shows many advantages when compared with other distributed algorithms proposed in the specific literature. In this way,...
In this contribution we explore the combination of bagging with random subspace and two variants of Battiti's mutual information feature selection methods to design fuzzy rule-based classification system ensembles. Besides, we consider a multicriteria genetic algorithm guided by the training error to select the component classifiers, in order to look for appropriate accuracy-complexity trade-offs...
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