This project consists in three main tasks: first, an analysis of the current state of the art in technologies for dealing with class imbalance problems in machine learning algorithms. Second, the analysis of how this problem actually affects a particular class of statistical models, the Bayesian Classifiers, proposing solutions to the particular problems found. And third, to implement a Bayesian Classifier and develop a series of experiments that would support the assertions of the analysis, and shed more light on how this problem can be dealt with.Este proyecto consta de tres tareas principales: primero, un análisis del estado actual de la técnica en tecnologías para tratar los problemas de desequilibrio de clase en algoritmos de aprendiz...
Abstract. There is an increasing interest in application of evolutionary algo-rithms to induce class...
AbstractThe use of Bayesian Networks (BNs) as classifiers in different fields of application has rec...
Abstract. Learning classifiers from imbalanced or skewed datasets is an important topic, arising ver...
The class-imbalance problem is the problem of learning a classification rule from data that are skew...
Modeling imbalanced data sets is a common problem in regression and classification where there is a ...
During the process of knowledge discovery in data, imbalanced learning data often emerges and presen...
En diversos problemas de reconocimiento de patrones, se ha observado que el desequilibrio de clases ...
In this report, I presented my results to the tasks of 2008 UC San Diego Data Mining Contest. This c...
In this paper, we present a new rule induction algorithm for machine learning in medical diagnosis. ...
En las últimas décadas, el aprendizaje automático ha adquirido importancia como una de las herramien...
In this paper we present an average-case analysis of the Bayesian classifier, a simple induction alg...
Abstract. Many real world datasets exhibit skewed class distributions in which almost all instances ...
0 problema de classificação em reconhecimento de padrões pode ser interpretado como um problema de e...
Imbalanced datasets are a well-known problem in data mining, where the datasets are composed of two ...
In binary classification, when the distribution of numbers in the class is imbalanced, we are aimed ...
Abstract. There is an increasing interest in application of evolutionary algo-rithms to induce class...
AbstractThe use of Bayesian Networks (BNs) as classifiers in different fields of application has rec...
Abstract. Learning classifiers from imbalanced or skewed datasets is an important topic, arising ver...
The class-imbalance problem is the problem of learning a classification rule from data that are skew...
Modeling imbalanced data sets is a common problem in regression and classification where there is a ...
During the process of knowledge discovery in data, imbalanced learning data often emerges and presen...
En diversos problemas de reconocimiento de patrones, se ha observado que el desequilibrio de clases ...
In this report, I presented my results to the tasks of 2008 UC San Diego Data Mining Contest. This c...
In this paper, we present a new rule induction algorithm for machine learning in medical diagnosis. ...
En las últimas décadas, el aprendizaje automático ha adquirido importancia como una de las herramien...
In this paper we present an average-case analysis of the Bayesian classifier, a simple induction alg...
Abstract. Many real world datasets exhibit skewed class distributions in which almost all instances ...
0 problema de classificação em reconhecimento de padrões pode ser interpretado como um problema de e...
Imbalanced datasets are a well-known problem in data mining, where the datasets are composed of two ...
In binary classification, when the distribution of numbers in the class is imbalanced, we are aimed ...
Abstract. There is an increasing interest in application of evolutionary algo-rithms to induce class...
AbstractThe use of Bayesian Networks (BNs) as classifiers in different fields of application has rec...
Abstract. Learning classifiers from imbalanced or skewed datasets is an important topic, arising ver...