The Experts below are selected from a list of 7269 Experts worldwide ranked by ideXlab platform
Luzia Vidal De Souza - One of the best experts on this subject based on the ideXlab platform.
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Applying correlation to enhance Boosting Technique using genetic programming as base learner
Applied Intelligence, 2009Co-Authors: Luzia Vidal De Souza, Aurora Pozo, Joel Mauricio Da Rosa, Anselmo Chaves NetoAbstract:This paper explores the Genetic Programming and Boosting Technique to obtain an ensemble of regressors and proposes a new formula for the updating of weights, as well as for the final hypothesis. Differently from studies found in the literature, in this paper we investigate the use of the correlation metric as an additional factor for the error metric. This new approach, called Boosting using Correlation Coefficients (BCC) has been empirically obtained after trying to improve the results of the other methods. To validate this method, we conducted two groups of experiments. In the first group, we explore the BCC for time series forecasting, in academic series and in a widespread Monte Carlo simulation covering the entire ARMA spectrum. The Genetic Programming (GP) is used as a base learner and the mean squared error (MSE) has been used to compare the accuracy of the proposed method against the results obtained by GP, GP using traditional Boosting and the traditional statistical methodology (ARMA). The second group of experiments aims at evaluating the proposed method on multivariate regression problems by choosing Cart (Classification and Regression Tree) as the base learner.
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the Boosting Technique using correlation coefficient to improve time series forecasting accuracy
Congress on Evolutionary Computation, 2007Co-Authors: Luzia Vidal De Souza, Aurora Pozo, Joel Mauricio Da Rosa, Arnolfo De Carvalho NetoAbstract:Time series forecasting has been considered an important tool to support decisions in different domains. A highly accurate prediction is essential to ensure the quality of these decisions. Time series forecasting is based on historical data and the predictions are usually made using statistical methods. These characteristics make the forecasting problem an interesting application of machine learning Techniques, especially for Boosting Techniques and genetic programming. Boosting Techniques currently receive a lot of attention; they combine predictions from different forecasting methods as a procedure to improve the accuracy. This paper explores genetic programming (GP) and Boosting Technique to obtain an ensemble of regressors and proposes a new formula for the updating of the weights and for the final hypothesis. This new formula is based on the correlation coefficient instead of the loss function used by traditional Boosting algorithms, this new algorithm is called Boosting using correlation coefficient (BCC). To validate this method, experiments were accomplished using real, financial and artificial series generated by Monte Carlo simulation. The results obtained by using this new methodology were compared with the results obtained from GP, GPBoost and the traditional statistical methodology (ARMA). The results show advantages in the use of the proposed approach.
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ISDA - An Empirical Study of Time Series Forecasting Using Boosting Technique with Correlation Coefficient
Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007), 2007Co-Authors: Luzia Vidal De Souza, Aurora Pozo, Arnolfo De Carvalho Neto, Joel Mauricio Da RosaAbstract:One of the most important fields of researches and applications is time series forecasting. The task to find a model that can fit the data is not easy, because the most of the problems the series are complex and noisy. Recently, ensemble of machines had been used to get accurate predictions. The mean idea is to combine predictions from different forecast methods in only one predictor and in this way to improve the accuracy. This paper explores genetic programming (GP) and Boosting Technique to obtain an ensemble of predictors and proposes a new approach to the Boosting algorithm where the correlation coefficients are used to update the weights and the final hypothesis instead of the loss function used traditionally by the Boosting algorithm. To validate this method, experiments were accomplished using real and artificial series generated by Monte Carlo simulation. The results obtained by using this new methodology was compared with the results obtained from GP, GPBoost and the traditional statistical methodology (ARMA).
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IEEE Congress on Evolutionary Computation - The Boosting Technique using correlation coefficient to improve time series forecasting accuracy
2007 IEEE Congress on Evolutionary Computation, 2007Co-Authors: Luzia Vidal De Souza, Aurora Pozo, Joel Mauricio Da Rosa, Arnolfo De Carvalho NetoAbstract:Time series forecasting has been considered an important tool to support decisions in different domains. A highly accurate prediction is essential to ensure the quality of these decisions. Time series forecasting is based on historical data and the predictions are usually made using statistical methods. These characteristics make the forecasting problem an interesting application of machine learning Techniques, especially for Boosting Techniques and genetic programming. Boosting Techniques currently receive a lot of attention; they combine predictions from different forecasting methods as a procedure to improve the accuracy. This paper explores genetic programming (GP) and Boosting Technique to obtain an ensemble of regressors and proposes a new formula for the updating of the weights and for the final hypothesis. This new formula is based on the correlation coefficient instead of the loss function used by traditional Boosting algorithms, this new algorithm is called Boosting using correlation coefficient (BCC). To validate this method, experiments were accomplished using real, financial and artificial series generated by Monte Carlo simulation. The results obtained by using this new methodology were compared with the results obtained from GP, GPBoost and the traditional statistical methodology (ARMA). The results show advantages in the use of the proposed approach.
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using correlation to improve Boosting Technique an application for time series forecasting
International Conference on Tools with Artificial Intelligence, 2006Co-Authors: Luzia Vidal De Souza, Aurora Pozo, Anselmo Chaves NetoAbstract:Time series forecasting has been widely used to support decision making, in this context a highly accurate prediction is essential to ensure the quality of the decisions. Ensembles of machines currently receive a lot of attention; they combine predictions from different forecasting methods as a procedure to improve the accuracy. This paper explores Genetic Programming and Boosting Technique to obtain an ensemble of regressors and proposes a new formula for the final hypothesis. This new formula is based on the correlation coefficient instead of the geometric median used by the Boosting algorithm. To validate this method, experiments were performed, the mean squared error (MSE) has been used to compare the accuracy of the proposed method against the results obtained by GP, GP using a Boosting Technique and the traditional statistical methodology (ARMA). The results show advantages in the use of the proposed approach.
Arnolfo De Carvalho Neto - One of the best experts on this subject based on the ideXlab platform.
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the Boosting Technique using correlation coefficient to improve time series forecasting accuracy
Congress on Evolutionary Computation, 2007Co-Authors: Luzia Vidal De Souza, Aurora Pozo, Joel Mauricio Da Rosa, Arnolfo De Carvalho NetoAbstract:Time series forecasting has been considered an important tool to support decisions in different domains. A highly accurate prediction is essential to ensure the quality of these decisions. Time series forecasting is based on historical data and the predictions are usually made using statistical methods. These characteristics make the forecasting problem an interesting application of machine learning Techniques, especially for Boosting Techniques and genetic programming. Boosting Techniques currently receive a lot of attention; they combine predictions from different forecasting methods as a procedure to improve the accuracy. This paper explores genetic programming (GP) and Boosting Technique to obtain an ensemble of regressors and proposes a new formula for the updating of the weights and for the final hypothesis. This new formula is based on the correlation coefficient instead of the loss function used by traditional Boosting algorithms, this new algorithm is called Boosting using correlation coefficient (BCC). To validate this method, experiments were accomplished using real, financial and artificial series generated by Monte Carlo simulation. The results obtained by using this new methodology were compared with the results obtained from GP, GPBoost and the traditional statistical methodology (ARMA). The results show advantages in the use of the proposed approach.
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ISDA - An Empirical Study of Time Series Forecasting Using Boosting Technique with Correlation Coefficient
Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007), 2007Co-Authors: Luzia Vidal De Souza, Aurora Pozo, Arnolfo De Carvalho Neto, Joel Mauricio Da RosaAbstract:One of the most important fields of researches and applications is time series forecasting. The task to find a model that can fit the data is not easy, because the most of the problems the series are complex and noisy. Recently, ensemble of machines had been used to get accurate predictions. The mean idea is to combine predictions from different forecast methods in only one predictor and in this way to improve the accuracy. This paper explores genetic programming (GP) and Boosting Technique to obtain an ensemble of predictors and proposes a new approach to the Boosting algorithm where the correlation coefficients are used to update the weights and the final hypothesis instead of the loss function used traditionally by the Boosting algorithm. To validate this method, experiments were accomplished using real and artificial series generated by Monte Carlo simulation. The results obtained by using this new methodology was compared with the results obtained from GP, GPBoost and the traditional statistical methodology (ARMA).
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IEEE Congress on Evolutionary Computation - The Boosting Technique using correlation coefficient to improve time series forecasting accuracy
2007 IEEE Congress on Evolutionary Computation, 2007Co-Authors: Luzia Vidal De Souza, Aurora Pozo, Joel Mauricio Da Rosa, Arnolfo De Carvalho NetoAbstract:Time series forecasting has been considered an important tool to support decisions in different domains. A highly accurate prediction is essential to ensure the quality of these decisions. Time series forecasting is based on historical data and the predictions are usually made using statistical methods. These characteristics make the forecasting problem an interesting application of machine learning Techniques, especially for Boosting Techniques and genetic programming. Boosting Techniques currently receive a lot of attention; they combine predictions from different forecasting methods as a procedure to improve the accuracy. This paper explores genetic programming (GP) and Boosting Technique to obtain an ensemble of regressors and proposes a new formula for the updating of the weights and for the final hypothesis. This new formula is based on the correlation coefficient instead of the loss function used by traditional Boosting algorithms, this new algorithm is called Boosting using correlation coefficient (BCC). To validate this method, experiments were accomplished using real, financial and artificial series generated by Monte Carlo simulation. The results obtained by using this new methodology were compared with the results obtained from GP, GPBoost and the traditional statistical methodology (ARMA). The results show advantages in the use of the proposed approach.
Aurora Pozo - One of the best experts on this subject based on the ideXlab platform.
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Applying correlation to enhance Boosting Technique using genetic programming as base learner
Applied Intelligence, 2009Co-Authors: Luzia Vidal De Souza, Aurora Pozo, Joel Mauricio Da Rosa, Anselmo Chaves NetoAbstract:This paper explores the Genetic Programming and Boosting Technique to obtain an ensemble of regressors and proposes a new formula for the updating of weights, as well as for the final hypothesis. Differently from studies found in the literature, in this paper we investigate the use of the correlation metric as an additional factor for the error metric. This new approach, called Boosting using Correlation Coefficients (BCC) has been empirically obtained after trying to improve the results of the other methods. To validate this method, we conducted two groups of experiments. In the first group, we explore the BCC for time series forecasting, in academic series and in a widespread Monte Carlo simulation covering the entire ARMA spectrum. The Genetic Programming (GP) is used as a base learner and the mean squared error (MSE) has been used to compare the accuracy of the proposed method against the results obtained by GP, GP using traditional Boosting and the traditional statistical methodology (ARMA). The second group of experiments aims at evaluating the proposed method on multivariate regression problems by choosing Cart (Classification and Regression Tree) as the base learner.
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the Boosting Technique using correlation coefficient to improve time series forecasting accuracy
Congress on Evolutionary Computation, 2007Co-Authors: Luzia Vidal De Souza, Aurora Pozo, Joel Mauricio Da Rosa, Arnolfo De Carvalho NetoAbstract:Time series forecasting has been considered an important tool to support decisions in different domains. A highly accurate prediction is essential to ensure the quality of these decisions. Time series forecasting is based on historical data and the predictions are usually made using statistical methods. These characteristics make the forecasting problem an interesting application of machine learning Techniques, especially for Boosting Techniques and genetic programming. Boosting Techniques currently receive a lot of attention; they combine predictions from different forecasting methods as a procedure to improve the accuracy. This paper explores genetic programming (GP) and Boosting Technique to obtain an ensemble of regressors and proposes a new formula for the updating of the weights and for the final hypothesis. This new formula is based on the correlation coefficient instead of the loss function used by traditional Boosting algorithms, this new algorithm is called Boosting using correlation coefficient (BCC). To validate this method, experiments were accomplished using real, financial and artificial series generated by Monte Carlo simulation. The results obtained by using this new methodology were compared with the results obtained from GP, GPBoost and the traditional statistical methodology (ARMA). The results show advantages in the use of the proposed approach.
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ISDA - An Empirical Study of Time Series Forecasting Using Boosting Technique with Correlation Coefficient
Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007), 2007Co-Authors: Luzia Vidal De Souza, Aurora Pozo, Arnolfo De Carvalho Neto, Joel Mauricio Da RosaAbstract:One of the most important fields of researches and applications is time series forecasting. The task to find a model that can fit the data is not easy, because the most of the problems the series are complex and noisy. Recently, ensemble of machines had been used to get accurate predictions. The mean idea is to combine predictions from different forecast methods in only one predictor and in this way to improve the accuracy. This paper explores genetic programming (GP) and Boosting Technique to obtain an ensemble of predictors and proposes a new approach to the Boosting algorithm where the correlation coefficients are used to update the weights and the final hypothesis instead of the loss function used traditionally by the Boosting algorithm. To validate this method, experiments were accomplished using real and artificial series generated by Monte Carlo simulation. The results obtained by using this new methodology was compared with the results obtained from GP, GPBoost and the traditional statistical methodology (ARMA).
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IEEE Congress on Evolutionary Computation - The Boosting Technique using correlation coefficient to improve time series forecasting accuracy
2007 IEEE Congress on Evolutionary Computation, 2007Co-Authors: Luzia Vidal De Souza, Aurora Pozo, Joel Mauricio Da Rosa, Arnolfo De Carvalho NetoAbstract:Time series forecasting has been considered an important tool to support decisions in different domains. A highly accurate prediction is essential to ensure the quality of these decisions. Time series forecasting is based on historical data and the predictions are usually made using statistical methods. These characteristics make the forecasting problem an interesting application of machine learning Techniques, especially for Boosting Techniques and genetic programming. Boosting Techniques currently receive a lot of attention; they combine predictions from different forecasting methods as a procedure to improve the accuracy. This paper explores genetic programming (GP) and Boosting Technique to obtain an ensemble of regressors and proposes a new formula for the updating of the weights and for the final hypothesis. This new formula is based on the correlation coefficient instead of the loss function used by traditional Boosting algorithms, this new algorithm is called Boosting using correlation coefficient (BCC). To validate this method, experiments were accomplished using real, financial and artificial series generated by Monte Carlo simulation. The results obtained by using this new methodology were compared with the results obtained from GP, GPBoost and the traditional statistical methodology (ARMA). The results show advantages in the use of the proposed approach.
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using correlation to improve Boosting Technique an application for time series forecasting
International Conference on Tools with Artificial Intelligence, 2006Co-Authors: Luzia Vidal De Souza, Aurora Pozo, Anselmo Chaves NetoAbstract:Time series forecasting has been widely used to support decision making, in this context a highly accurate prediction is essential to ensure the quality of the decisions. Ensembles of machines currently receive a lot of attention; they combine predictions from different forecasting methods as a procedure to improve the accuracy. This paper explores Genetic Programming and Boosting Technique to obtain an ensemble of regressors and proposes a new formula for the final hypothesis. This new formula is based on the correlation coefficient instead of the geometric median used by the Boosting algorithm. To validate this method, experiments were performed, the mean squared error (MSE) has been used to compare the accuracy of the proposed method against the results obtained by GP, GP using a Boosting Technique and the traditional statistical methodology (ARMA). The results show advantages in the use of the proposed approach.
Joel Mauricio Da Rosa - One of the best experts on this subject based on the ideXlab platform.
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Applying correlation to enhance Boosting Technique using genetic programming as base learner
Applied Intelligence, 2009Co-Authors: Luzia Vidal De Souza, Aurora Pozo, Joel Mauricio Da Rosa, Anselmo Chaves NetoAbstract:This paper explores the Genetic Programming and Boosting Technique to obtain an ensemble of regressors and proposes a new formula for the updating of weights, as well as for the final hypothesis. Differently from studies found in the literature, in this paper we investigate the use of the correlation metric as an additional factor for the error metric. This new approach, called Boosting using Correlation Coefficients (BCC) has been empirically obtained after trying to improve the results of the other methods. To validate this method, we conducted two groups of experiments. In the first group, we explore the BCC for time series forecasting, in academic series and in a widespread Monte Carlo simulation covering the entire ARMA spectrum. The Genetic Programming (GP) is used as a base learner and the mean squared error (MSE) has been used to compare the accuracy of the proposed method against the results obtained by GP, GP using traditional Boosting and the traditional statistical methodology (ARMA). The second group of experiments aims at evaluating the proposed method on multivariate regression problems by choosing Cart (Classification and Regression Tree) as the base learner.
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the Boosting Technique using correlation coefficient to improve time series forecasting accuracy
Congress on Evolutionary Computation, 2007Co-Authors: Luzia Vidal De Souza, Aurora Pozo, Joel Mauricio Da Rosa, Arnolfo De Carvalho NetoAbstract:Time series forecasting has been considered an important tool to support decisions in different domains. A highly accurate prediction is essential to ensure the quality of these decisions. Time series forecasting is based on historical data and the predictions are usually made using statistical methods. These characteristics make the forecasting problem an interesting application of machine learning Techniques, especially for Boosting Techniques and genetic programming. Boosting Techniques currently receive a lot of attention; they combine predictions from different forecasting methods as a procedure to improve the accuracy. This paper explores genetic programming (GP) and Boosting Technique to obtain an ensemble of regressors and proposes a new formula for the updating of the weights and for the final hypothesis. This new formula is based on the correlation coefficient instead of the loss function used by traditional Boosting algorithms, this new algorithm is called Boosting using correlation coefficient (BCC). To validate this method, experiments were accomplished using real, financial and artificial series generated by Monte Carlo simulation. The results obtained by using this new methodology were compared with the results obtained from GP, GPBoost and the traditional statistical methodology (ARMA). The results show advantages in the use of the proposed approach.
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ISDA - An Empirical Study of Time Series Forecasting Using Boosting Technique with Correlation Coefficient
Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007), 2007Co-Authors: Luzia Vidal De Souza, Aurora Pozo, Arnolfo De Carvalho Neto, Joel Mauricio Da RosaAbstract:One of the most important fields of researches and applications is time series forecasting. The task to find a model that can fit the data is not easy, because the most of the problems the series are complex and noisy. Recently, ensemble of machines had been used to get accurate predictions. The mean idea is to combine predictions from different forecast methods in only one predictor and in this way to improve the accuracy. This paper explores genetic programming (GP) and Boosting Technique to obtain an ensemble of predictors and proposes a new approach to the Boosting algorithm where the correlation coefficients are used to update the weights and the final hypothesis instead of the loss function used traditionally by the Boosting algorithm. To validate this method, experiments were accomplished using real and artificial series generated by Monte Carlo simulation. The results obtained by using this new methodology was compared with the results obtained from GP, GPBoost and the traditional statistical methodology (ARMA).
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IEEE Congress on Evolutionary Computation - The Boosting Technique using correlation coefficient to improve time series forecasting accuracy
2007 IEEE Congress on Evolutionary Computation, 2007Co-Authors: Luzia Vidal De Souza, Aurora Pozo, Joel Mauricio Da Rosa, Arnolfo De Carvalho NetoAbstract:Time series forecasting has been considered an important tool to support decisions in different domains. A highly accurate prediction is essential to ensure the quality of these decisions. Time series forecasting is based on historical data and the predictions are usually made using statistical methods. These characteristics make the forecasting problem an interesting application of machine learning Techniques, especially for Boosting Techniques and genetic programming. Boosting Techniques currently receive a lot of attention; they combine predictions from different forecasting methods as a procedure to improve the accuracy. This paper explores genetic programming (GP) and Boosting Technique to obtain an ensemble of regressors and proposes a new formula for the updating of the weights and for the final hypothesis. This new formula is based on the correlation coefficient instead of the loss function used by traditional Boosting algorithms, this new algorithm is called Boosting using correlation coefficient (BCC). To validate this method, experiments were accomplished using real, financial and artificial series generated by Monte Carlo simulation. The results obtained by using this new methodology were compared with the results obtained from GP, GPBoost and the traditional statistical methodology (ARMA). The results show advantages in the use of the proposed approach.
Anselmo Chaves Neto - One of the best experts on this subject based on the ideXlab platform.
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Applying correlation to enhance Boosting Technique using genetic programming as base learner
Applied Intelligence, 2009Co-Authors: Luzia Vidal De Souza, Aurora Pozo, Joel Mauricio Da Rosa, Anselmo Chaves NetoAbstract:This paper explores the Genetic Programming and Boosting Technique to obtain an ensemble of regressors and proposes a new formula for the updating of weights, as well as for the final hypothesis. Differently from studies found in the literature, in this paper we investigate the use of the correlation metric as an additional factor for the error metric. This new approach, called Boosting using Correlation Coefficients (BCC) has been empirically obtained after trying to improve the results of the other methods. To validate this method, we conducted two groups of experiments. In the first group, we explore the BCC for time series forecasting, in academic series and in a widespread Monte Carlo simulation covering the entire ARMA spectrum. The Genetic Programming (GP) is used as a base learner and the mean squared error (MSE) has been used to compare the accuracy of the proposed method against the results obtained by GP, GP using traditional Boosting and the traditional statistical methodology (ARMA). The second group of experiments aims at evaluating the proposed method on multivariate regression problems by choosing Cart (Classification and Regression Tree) as the base learner.
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using correlation to improve Boosting Technique an application for time series forecasting
International Conference on Tools with Artificial Intelligence, 2006Co-Authors: Luzia Vidal De Souza, Aurora Pozo, Anselmo Chaves NetoAbstract:Time series forecasting has been widely used to support decision making, in this context a highly accurate prediction is essential to ensure the quality of the decisions. Ensembles of machines currently receive a lot of attention; they combine predictions from different forecasting methods as a procedure to improve the accuracy. This paper explores Genetic Programming and Boosting Technique to obtain an ensemble of regressors and proposes a new formula for the final hypothesis. This new formula is based on the correlation coefficient instead of the geometric median used by the Boosting algorithm. To validate this method, experiments were performed, the mean squared error (MSE) has been used to compare the accuracy of the proposed method against the results obtained by GP, GP using a Boosting Technique and the traditional statistical methodology (ARMA). The results show advantages in the use of the proposed approach.
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ICTAI - Using Correlation to Improve Boosting Technique: An Application for Time Series Forecasting
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06), 2006Co-Authors: Luzia Vidal De Souza, Aurora Pozo, Anselmo Chaves NetoAbstract:Time series forecasting has been widely used to support decision making, in this context a highly accurate prediction is essential to ensure the quality of the decisions. Ensembles of machines currently receive a lot of attention; they combine predictions from different forecasting methods as a procedure to improve the accuracy. This paper explores Genetic Programming and Boosting Technique to obtain an ensemble of regressors and proposes a new formula for the final hypothesis. This new formula is based on the correlation coefficient instead of the geometric median used by the Boosting algorithm. To validate this method, experiments were performed, the mean squared error (MSE) has been used to compare the accuracy of the proposed method against the results obtained by GP, GP using a Boosting Technique and the traditional statistical methodology (ARMA). The results show advantages in the use of the proposed approach.