The Experts below are selected from a list of 13710 Experts worldwide ranked by ideXlab platform
Serafín Moral - One of the best experts on this subject based on the ideXlab platform.
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A memory efficient semi-Naive Bayes Classifier with grouping of cases
Intelligent Data Analysis, 2011Co-Authors: Joaquín Abellán, Andrés R. Masegosa, Andrés Cano, Serafín MoralAbstract:Previous analysis of learning data can help us to discover hidden relations among features. We can use this knowledge to select the most suitable learning methods and to achieve further improvements in the performance of classification systems. For the known Naive Bayes Classifier, several studies have been conducted in an attempt to reconstruct the set of attributes in order to remove or debilitate dependence relations, which can reduce the accuracy of this Classifier. These methods are included in the ones known as semi-Naive Bayes Classifiers. In the present research, we present a semi-Naive Bayes Classifier that searches for dependent attributes using a filter approach. In order to prevent the number of cases of the compound attributes from being excessively high, a grouping procedure is always applied after the merging of two variables. This method attempts to group two or more cases of the new variable into a single one, in order to reduce the cardinality of the compound variables. As a result, the model presented is a competitive Classifier with respect to the state of the art of semi-Naive Bayes Classifiers, particularly in terms of quality of class probability estimates, but with a much lower memory space complexity.
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ECSQARU - A Semi-Naive Bayes Classifier with Grouping of Cases
Lecture Notes in Computer Science, 2007Co-Authors: Joaquín Abellán, Andrés R. Masegosa, Andrés Cano, Serafín MoralAbstract:In this work, we present a semi-Naive Bayes Classifier that searches for dependent attributes using different filter approaches. In order to avoid that the number of cases of the compound attributes be too high, a grouping procedure is applied each time after two variables are merged. This method tries to group two or more cases of the new variable into an unique value. In an emperical study, we show as this approach outperforms the Naive Bayes Classifier in a very robust way and reaches the performance of the Pazzani's semi-Naive Bayes [1] without the high cost of a wrapper search.
Joaquín Abellán - One of the best experts on this subject based on the ideXlab platform.
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A memory efficient semi-Naive Bayes Classifier with grouping of cases
Intelligent Data Analysis, 2011Co-Authors: Joaquín Abellán, Andrés R. Masegosa, Andrés Cano, Serafín MoralAbstract:Previous analysis of learning data can help us to discover hidden relations among features. We can use this knowledge to select the most suitable learning methods and to achieve further improvements in the performance of classification systems. For the known Naive Bayes Classifier, several studies have been conducted in an attempt to reconstruct the set of attributes in order to remove or debilitate dependence relations, which can reduce the accuracy of this Classifier. These methods are included in the ones known as semi-Naive Bayes Classifiers. In the present research, we present a semi-Naive Bayes Classifier that searches for dependent attributes using a filter approach. In order to prevent the number of cases of the compound attributes from being excessively high, a grouping procedure is always applied after the merging of two variables. This method attempts to group two or more cases of the new variable into a single one, in order to reduce the cardinality of the compound variables. As a result, the model presented is a competitive Classifier with respect to the state of the art of semi-Naive Bayes Classifiers, particularly in terms of quality of class probability estimates, but with a much lower memory space complexity.
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ECSQARU - A Semi-Naive Bayes Classifier with Grouping of Cases
Lecture Notes in Computer Science, 2007Co-Authors: Joaquín Abellán, Andrés R. Masegosa, Andrés Cano, Serafín MoralAbstract:In this work, we present a semi-Naive Bayes Classifier that searches for dependent attributes using different filter approaches. In order to avoid that the number of cases of the compound attributes be too high, a grouping procedure is applied each time after two variables are merged. This method tries to group two or more cases of the new variable into an unique value. In an emperical study, we show as this approach outperforms the Naive Bayes Classifier in a very robust way and reaches the performance of the Pazzani's semi-Naive Bayes [1] without the high cost of a wrapper search.
Tao Lu - One of the best experts on this subject based on the ideXlab platform.
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prediction of protein protein interaction sites based on Naive Bayes Classifier
Biochemistry Research International, 2015Co-Authors: Haijiang Geng, Tao LuAbstract:Protein functions through interactions with other proteins and biomolecules and these interactions occur on the so-called interface residues of the protein sequences. Identifying interface residues makes us better understand the biological mechanism of protein interaction. Meanwhile, information about the interface residues contributes to the understanding of metabolic, signal transduction networks and indicates directions in drug designing. In recent years, researchers have focused on developing new computational methods for predicting protein interface residues. Here we creatively used a 181-dimension protein sequence feature vector as input to the Naive Bayes Classifier- (NBC-) based method to predict interaction sites in protein-protein complexes interaction. The prediction of interaction sites in protein interactions is regarded as an amino acid residue binary classification problem by applying NBC with protein sequence features. Independent test results suggested that Naive Bayes Classifier-based method with the protein sequence features as input vectors performed well.
Andrés R. Masegosa - One of the best experts on this subject based on the ideXlab platform.
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A memory efficient semi-Naive Bayes Classifier with grouping of cases
Intelligent Data Analysis, 2011Co-Authors: Joaquín Abellán, Andrés R. Masegosa, Andrés Cano, Serafín MoralAbstract:Previous analysis of learning data can help us to discover hidden relations among features. We can use this knowledge to select the most suitable learning methods and to achieve further improvements in the performance of classification systems. For the known Naive Bayes Classifier, several studies have been conducted in an attempt to reconstruct the set of attributes in order to remove or debilitate dependence relations, which can reduce the accuracy of this Classifier. These methods are included in the ones known as semi-Naive Bayes Classifiers. In the present research, we present a semi-Naive Bayes Classifier that searches for dependent attributes using a filter approach. In order to prevent the number of cases of the compound attributes from being excessively high, a grouping procedure is always applied after the merging of two variables. This method attempts to group two or more cases of the new variable into a single one, in order to reduce the cardinality of the compound variables. As a result, the model presented is a competitive Classifier with respect to the state of the art of semi-Naive Bayes Classifiers, particularly in terms of quality of class probability estimates, but with a much lower memory space complexity.
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ECSQARU - A Semi-Naive Bayes Classifier with Grouping of Cases
Lecture Notes in Computer Science, 2007Co-Authors: Joaquín Abellán, Andrés R. Masegosa, Andrés Cano, Serafín MoralAbstract:In this work, we present a semi-Naive Bayes Classifier that searches for dependent attributes using different filter approaches. In order to avoid that the number of cases of the compound attributes be too high, a grouping procedure is applied each time after two variables are merged. This method tries to group two or more cases of the new variable into an unique value. In an emperical study, we show as this approach outperforms the Naive Bayes Classifier in a very robust way and reaches the performance of the Pazzani's semi-Naive Bayes [1] without the high cost of a wrapper search.
Andrés Cano - One of the best experts on this subject based on the ideXlab platform.
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A memory efficient semi-Naive Bayes Classifier with grouping of cases
Intelligent Data Analysis, 2011Co-Authors: Joaquín Abellán, Andrés R. Masegosa, Andrés Cano, Serafín MoralAbstract:Previous analysis of learning data can help us to discover hidden relations among features. We can use this knowledge to select the most suitable learning methods and to achieve further improvements in the performance of classification systems. For the known Naive Bayes Classifier, several studies have been conducted in an attempt to reconstruct the set of attributes in order to remove or debilitate dependence relations, which can reduce the accuracy of this Classifier. These methods are included in the ones known as semi-Naive Bayes Classifiers. In the present research, we present a semi-Naive Bayes Classifier that searches for dependent attributes using a filter approach. In order to prevent the number of cases of the compound attributes from being excessively high, a grouping procedure is always applied after the merging of two variables. This method attempts to group two or more cases of the new variable into a single one, in order to reduce the cardinality of the compound variables. As a result, the model presented is a competitive Classifier with respect to the state of the art of semi-Naive Bayes Classifiers, particularly in terms of quality of class probability estimates, but with a much lower memory space complexity.
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ECSQARU - A Semi-Naive Bayes Classifier with Grouping of Cases
Lecture Notes in Computer Science, 2007Co-Authors: Joaquín Abellán, Andrés R. Masegosa, Andrés Cano, Serafín MoralAbstract:In this work, we present a semi-Naive Bayes Classifier that searches for dependent attributes using different filter approaches. In order to avoid that the number of cases of the compound attributes be too high, a grouping procedure is applied each time after two variables are merged. This method tries to group two or more cases of the new variable into an unique value. In an emperical study, we show as this approach outperforms the Naive Bayes Classifier in a very robust way and reaches the performance of the Pazzani's semi-Naive Bayes [1] without the high cost of a wrapper search.
