The Experts below are selected from a list of 1860 Experts worldwide ranked by ideXlab platform
André C. P. L. F. De Carvalho - One of the best experts on this subject based on the ideXlab platform.
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Building binary-tree-based Multiclass classifiers using separability measures
Neurocomputing, 2020Co-Authors: Ana Carolina Lorena, André C. P. L. F. De CarvalhoAbstract:Various popular machine learning techniques, like support vector machines, are originally conceived for the solution of two-class (binary) classification Problems. However, a large number of real Problems present more than two classes. A common approach to generalize binary learning techniques to solve Problems with more than two classes, also known as Multiclass classification Problems, consists of hierarchically decomposing the Multiclass Problem into multiple binary sub-Problems, whose outputs are combined to define the predicted class. This strategy results in a tree of binary classifiers, where each internal node corresponds to a binary classifier distinguishing two groups of classes and the leaf nodes correspond to the Problem classes. This paper investigates how measures of the separability between classes can be employed in the construction of binary-tree-based Multiclass classifiers, adapting the decompositions performed to each particular Multiclass Problem. (C) 2010 Elsevier B.V. All rights reserved
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Evolutionary tuning of SVM parameter values in Multiclass Problems
Neurocomputing, 2020Co-Authors: Ana Carolina Lorena, André C. P. L. F. De CarvalhoAbstract:Support vector machines (SVMs) were originally formulated for the solution of binary classification Problems. In Multiclass Problems, a decomposition approach is often employed, in which the Multiclass Problem is divided into multiple binary subProblems, whose results are combined. Generally, the performance of SVM classifiers is affected by the selection of values for their parameters. This paper investigates the use of genetic algorithms (GAs) to tune the parameters of the binary SVMs in common Multiclass decompositions. The developed GA may search for a set of parameter values common to all binary classifiers or for differentiated values for each binary classifier. (C) 2008 Elsevier B.V. All rights reserved
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FLAIRS Conference - Pursuing the Best ECOC Dimension for Multiclass Problems.
2020Co-Authors: Edgar Pimenta, João Gama, André C. P. L. F. De CarvalhoAbstract:Recent work highlights advantages in decomposing Multiclass decision Problems into multiple binary Problems. Several strategies have been proposed for this decomposition. The most frequently investigated are All-vs-All, One-vs-All and the Error correction output codes (ECOC). ECOC are binary words (codewords) and can be adapted to be used in classifications Problems. They must, however, comply with some specific constraints. The codewords can have several dimensions for each number of classes to be represented. These dimensions grow exponentially with the number of classes of the Multiclass Problem. Two methods to choose the dimension of a ECOC, which assure a good trade-off between redundancy and error correction capacity, are proposed in this paper. The methods are evaluated in a set of benchmark classification Problems. Experimental results show that they are competitive against conventional Multiclass decomposi-
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Building binary-tree-based Multiclass classifiers using separability measures
Neurocomputing, 2010Co-Authors: Ana Carolina Lorena, André C. P. L. F. De CarvalhoAbstract:Various popular machine learning techniques, like support vector machines, are originally conceived for the solution of two-class (binary) classification Problems. However, a large number of real Problems present more than two classes. A common approach to generalize binary learning techniques to solve Problems with more than two classes, also known as Multiclass classification Problems, consists of hierarchically decomposing the Multiclass Problem into multiple binary sub-Problems, whose outputs are combined to define the predicted class. This strategy results in a tree of binary classifiers, where each internal node corresponds to a binary classifier distinguishing two groups of classes and the leaf nodes correspond to the Problem classes. This paper investigates how measures of the separability between classes can be employed in the construction of binary-tree-based Multiclass classifiers, adapting the decompositions performed to each particular Multiclass Problem.
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A review on the combination of binary classifiers in Multiclass Problems
Artificial Intelligence Review, 2009Co-Authors: Ana Carolina Lorena, André C. P. L. F. De Carvalho, João M. P. GamaAbstract:Several real Problems involve the classification of data into categories or classes. Given a data set containing data whose classes are known, Machine Learning algorithms can be employed for the induction of a classifier able to predict the class of new data from the same domain, performing the desired discrimination. Some learning techniques are originally conceived for the solution of Problems with only two classes, also named binary classification Problems. However, many Problems require the discrimination of examples into more than two categories or classes. This paper presents a survey on the main strategies for the generalization of binary classifiers to Problems with more than two classes, known as Multiclass classification Problems. The focus is on strategies that decompose the original Multiclass Problem into multiple binary subtasks, whose outputs are combined to obtain the final prediction.
Ana Carolina Lorena - One of the best experts on this subject based on the ideXlab platform.
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Building binary-tree-based Multiclass classifiers using separability measures
Neurocomputing, 2020Co-Authors: Ana Carolina Lorena, André C. P. L. F. De CarvalhoAbstract:Various popular machine learning techniques, like support vector machines, are originally conceived for the solution of two-class (binary) classification Problems. However, a large number of real Problems present more than two classes. A common approach to generalize binary learning techniques to solve Problems with more than two classes, also known as Multiclass classification Problems, consists of hierarchically decomposing the Multiclass Problem into multiple binary sub-Problems, whose outputs are combined to define the predicted class. This strategy results in a tree of binary classifiers, where each internal node corresponds to a binary classifier distinguishing two groups of classes and the leaf nodes correspond to the Problem classes. This paper investigates how measures of the separability between classes can be employed in the construction of binary-tree-based Multiclass classifiers, adapting the decompositions performed to each particular Multiclass Problem. (C) 2010 Elsevier B.V. All rights reserved
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Evolutionary tuning of SVM parameter values in Multiclass Problems
Neurocomputing, 2020Co-Authors: Ana Carolina Lorena, André C. P. L. F. De CarvalhoAbstract:Support vector machines (SVMs) were originally formulated for the solution of binary classification Problems. In Multiclass Problems, a decomposition approach is often employed, in which the Multiclass Problem is divided into multiple binary subProblems, whose results are combined. Generally, the performance of SVM classifiers is affected by the selection of values for their parameters. This paper investigates the use of genetic algorithms (GAs) to tune the parameters of the binary SVMs in common Multiclass decompositions. The developed GA may search for a set of parameter values common to all binary classifiers or for differentiated values for each binary classifier. (C) 2008 Elsevier B.V. All rights reserved
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Building binary-tree-based Multiclass classifiers using separability measures
Neurocomputing, 2010Co-Authors: Ana Carolina Lorena, André C. P. L. F. De CarvalhoAbstract:Various popular machine learning techniques, like support vector machines, are originally conceived for the solution of two-class (binary) classification Problems. However, a large number of real Problems present more than two classes. A common approach to generalize binary learning techniques to solve Problems with more than two classes, also known as Multiclass classification Problems, consists of hierarchically decomposing the Multiclass Problem into multiple binary sub-Problems, whose outputs are combined to define the predicted class. This strategy results in a tree of binary classifiers, where each internal node corresponds to a binary classifier distinguishing two groups of classes and the leaf nodes correspond to the Problem classes. This paper investigates how measures of the separability between classes can be employed in the construction of binary-tree-based Multiclass classifiers, adapting the decompositions performed to each particular Multiclass Problem.
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A review on the combination of binary classifiers in Multiclass Problems
Artificial Intelligence Review, 2009Co-Authors: Ana Carolina Lorena, André C. P. L. F. De Carvalho, João M. P. GamaAbstract:Several real Problems involve the classification of data into categories or classes. Given a data set containing data whose classes are known, Machine Learning algorithms can be employed for the induction of a classifier able to predict the class of new data from the same domain, performing the desired discrimination. Some learning techniques are originally conceived for the solution of Problems with only two classes, also named binary classification Problems. However, many Problems require the discrimination of examples into more than two categories or classes. This paper presents a survey on the main strategies for the generalization of binary classifiers to Problems with more than two classes, known as Multiclass classification Problems. The focus is on strategies that decompose the original Multiclass Problem into multiple binary subtasks, whose outputs are combined to obtain the final prediction.
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Estratégias para a Combinação de Classificadores Binários em Soluções Multiclasses
Revista De Informática Teórica E Aplicada, 2008Co-Authors: Ana Carolina Lorena, André C. P. L. F. De CarvalhoAbstract:Several Problems involve the classification of data into categories, also called classes. Given a dataset containing data whose classes are known, Machine Learning algorithms can be employed for the induction of a classifier able to predict the class of new data from the same domain, performing the desired discrimination. Some learning techniques are originally conceived for the solution of Problems with only two classes, also named binary Problems. However, several Problems require the discrimination of examples into more than two categories or classes. This paper surveys strategies for the generalization of binary classifiers to Problems with more than two classes, known as Multiclass Problems. The focus is on strategies that decompose the original Multiclass Problem into multiple binary subtasks, whose outputs are combined to obtain the final classification.
Hyeran Byun - One of the best experts on this subject based on the ideXlab platform.
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combining svm classifiers for Multiclass Problem its application to face recognition
Lecture Notes in Computer Science, 2003Co-Authors: Jaepil Ko, Hyeran ByunAbstract:In face recognition, a simple classifier such as k-NN is frequently used. For a robust system, it is common to construct the multi-class classifier by combining the outputs of several binary ones. The two basic schemes for this purpose are known as one-per-class (OPC) and pairwise coupling (PWC). The performance of decomposition methods depends on accuracy of base dichotomizers. Support vector machine is suitable for this purpose. In this paper, we give the strength and weakness of two representative decomposition methods, OPC and PWC. We also introduce a new method combining OPC and PWC with rejection based on the analysis of OPC and PWC using SVM as base classifiers. The experimental results on the ORL face database show that our proposed method can reduce the error rate on the real dataset.
Elif M. Karsligil - One of the best experts on this subject based on the ideXlab platform.
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off line signature verification and recognition by support vector machine
European Signal Processing Conference, 2005Co-Authors: Emre Ozgunduz, Tulin Senturk, Elif M. KarsligilAbstract:In this paper we present an off-line signature verification and recognition system using the global, directional and grid fea-tures of signatures. Support Vector Machine (SVM) was used to verify and classify the signatures and a classification ratio of 0.95 was obtained. As the recognition of signatures repre-sents a Multiclass Problem SVM's one-against-all method was used. We also compare our methods performance with Artifical Neural Network's (ANN) backpropagation method.
Bon-woo Hwang - One of the best experts on this subject based on the ideXlab platform.
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ICPR - Retrieval of the top N matches with support vector machines
Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2000Co-Authors: Bon-woo HwangAbstract:Support vector machines (SVM) have been recently proposed for pattern recognition. Their basic property allows us to find a decision surface between two classes in terms of a hyperplane in a high dimensional space. In a Multiclass recognition Problem, SVM are used in the form of a combination of binary classifiers. However, SVM are unable to retrieve the top N matches, since they are designed to yield only one-the best match-in a Multiclass Problem. In other words, there is no proper similarity measurement for ordering all the classes in a given space using SVM. In this paper, we present an efficient method for the retrieval of the top N matches in a Multiclass Problem using SVM. For evaluation of the proposed method, we compared its result with that of a PCA algorithm in ranking the matches between classes.
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Retrieval of the top N matches with support vector machines
Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2000Co-Authors: Bon-woo HwangAbstract:Support vector machines (SVM) have been recently proposed for pattern recognition. Their basic property allows us to find a decision surface between two classes in terms of a hyperplane in a high dimensional space. In a Multiclass recognition Problem, SVM are used in the form of a combination of binary classifiers. However, SVM are unable to retrieve the top N matches, since they are designed to yield only one-the best match-in a Multiclass Problem. In other words, there is no proper similarity measurement for ordering all the classes in a given space using SVM. In this paper, we present an efficient method for the retrieval of the top N matches in a Multiclass Problem using SVM. For evaluation of the proposed method, we compared its result with that of a PCA algorithm in ranking the matches between classes.