The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Alex A Freitas - One of the best experts on this subject based on the ideXlab platform.
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Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets
IEEE Transactions on Evolutionary Computation, 2014Co-Authors: Rodrigo C Barros, Marcio P Basgalupp, Alex A Freitas, A C P L F De CarvalhoAbstract:Decision-Tree induction algorithms are widely used in machine learning applications in which the goal is to extract knowledge from data and present it in a graphically intuitive way. The most successful strategy for inducing Decision Trees is the greedy top-down recursive approach, which has been continuously improved by researchers over the past 40 years. In this paper, we propose a paradigm shift in the research of Decision Trees: instead of proposing a new manually designed method for inducing Decision Trees, we propose automatically designing Decision-Tree induction algorithms tailored to a specific type of classification data set (or application domain). Following recent breakthroughs in the automatic design of machine learning algorithms, we propose a hyper-heuristic evolutionary algorithm called hyper-heuristic evolutionary algorithm for designing Decision-Tree algorithms (HEAD-DT) that evolves design components of top-down Decision-Tree induction algorithms. By the end of the evolution, we expect HEAD-DT to generate a new and possibly better Decision-Tree algorithm for a given application domain. We perform extensive experiments in 35 real-world microarray gene expression data sets to assess the performance of HEAD-DT, and compare it with very well known Decision-Tree algorithms such as C4.5, CART, and REPTree. Results show that HEAD-DT is capable of generating algorithms that significantly outperform the baseline manually designed Decision-Tree algorithms regarding predictive accuracy and F-measure.
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a hyper heuristic evolutionary algorithm for automatically designing Decision Tree algorithms
Genetic and Evolutionary Computation Conference, 2012Co-Authors: Rodrigo C Barros, Marcio P Basgalupp, A C P L F De Carvalho, Alex A FreitasAbstract:Decision Tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans. The most successful strategy for inducing Decision Trees, the greedy top-down approach, has been continuously improved by researchers over the years. This work, following recent breakthroughs in the automatic design of machine learning algorithms, proposes a hyper-heuristic evolutionary algorithm for automatically generating Decision-Tree induction algorithms, named HEAD-DT. We perform extensive experiments in 20 public data sets to assess the performance of HEAD-DT, and we compare it to traditional Decision-Tree algorithms such as C4.5 and CART. Results show that HEAD-DT can generate algorithms that significantly outperform C4.5 and CART regarding predictive accuracy and F-Measure.
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a survey of evolutionary algorithms for Decision Tree induction
Systems Man and Cybernetics, 2012Co-Authors: Rodrigo C Barros, Marcio P Basgalupp, A C P L F De Carvalho, Alex A FreitasAbstract:This paper presents a survey of evolutionary algorithms that are designed for Decision-Tree induction. In this context, most of the paper focuses on approaches that evolve Decision Trees as an alternate heuristics to the traditional top-down divide-and-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of Decision-Tree classifiers. The paper's original contributions are the following. First, it provides an up-to-date overview that is fully focused on evolutionary algorithms and Decision Trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy, which addresses works that evolve Decision Trees and works that design Decision-Tree components by the use of evolutionary algorithms. Finally, a number of references are provided that describe applications of evolutionary algorithms for Decision-Tree induction in different domains. At the end of this paper, we address some important issues and open questions that can be the subject of future research.
W Reichl - One of the best experts on this subject based on the ideXlab platform.
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robust Decision Tree state tying for continuous speech recognition
IEEE Transactions on Speech and Audio Processing, 2000Co-Authors: W Reichl, Wu ChouAbstract:Methods of improving the robustness and accuracy of acoustic modeling using Decision Tree based state tying are described. A new two-level segmental clustering approach is devised which combines the Decision Tree based state tying with agglomerative clustering of rare acoustic phonetic events. In addition, a unified maximum likelihood framework for incorporating both phonetic and nonphonetic features in Decision Tree based state tying is presented. In contrast to other heuristic data separation methods, which often lead to training data depletion, a tagging scheme is used to attach various features of interest and the selection of these features in the Decision Tree is data driven. Finally, two methods of using multiple-mixture parameterization to improve the quality of the evaluation function in Decision Tree state tying are described. One method is based on the approach of k-means fitting and the other method is based on a novel use of a local multilevel optimal subTree. Both methods provide more accurate likelihood evaluation in Decision Tree clustering and are consistent with the structure of the Decision Tree. Experimental results on Wall STreet Journal corpora demonstrate that the proposed approaches lead to a significant improvement in model quality and recognition performance.
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Decision Tree state tying based on penalized bayesian information criterion
International Conference on Acoustics Speech and Signal Processing, 1999Co-Authors: Wu Chou, W ReichlAbstract:In this paper, an approach of the penalized Bayesian information criterion (pBIC) for Decision Tree state tying is described. The pBIC is applied to two important applications. First, it is used as a Decision Tree growing criterion in place of the conventional approach of using a heuristic constant threshold. It is found that original BIC penalty is too low and will not lead to a compact Decision Tree state tying model. Based on Wolfe's modification to the asymptotic null distribution, it is derived that two times BIC penalty should be used for Decision Tree state tying based on pBIC. Secondly, pBIC is studied as a model compression criterion for Decision Tree state tying based acoustic modeling. Experimental results on a large vocabulary (Wall STreet Journal) speech recognition task indicate that a compact Decision Tree could be achieved with almost no loss of the speech recognition performance.
Wu Chou - One of the best experts on this subject based on the ideXlab platform.
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robust Decision Tree state tying for continuous speech recognition
IEEE Transactions on Speech and Audio Processing, 2000Co-Authors: W Reichl, Wu ChouAbstract:Methods of improving the robustness and accuracy of acoustic modeling using Decision Tree based state tying are described. A new two-level segmental clustering approach is devised which combines the Decision Tree based state tying with agglomerative clustering of rare acoustic phonetic events. In addition, a unified maximum likelihood framework for incorporating both phonetic and nonphonetic features in Decision Tree based state tying is presented. In contrast to other heuristic data separation methods, which often lead to training data depletion, a tagging scheme is used to attach various features of interest and the selection of these features in the Decision Tree is data driven. Finally, two methods of using multiple-mixture parameterization to improve the quality of the evaluation function in Decision Tree state tying are described. One method is based on the approach of k-means fitting and the other method is based on a novel use of a local multilevel optimal subTree. Both methods provide more accurate likelihood evaluation in Decision Tree clustering and are consistent with the structure of the Decision Tree. Experimental results on Wall STreet Journal corpora demonstrate that the proposed approaches lead to a significant improvement in model quality and recognition performance.
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Decision Tree state tying based on penalized bayesian information criterion
International Conference on Acoustics Speech and Signal Processing, 1999Co-Authors: Wu Chou, W ReichlAbstract:In this paper, an approach of the penalized Bayesian information criterion (pBIC) for Decision Tree state tying is described. The pBIC is applied to two important applications. First, it is used as a Decision Tree growing criterion in place of the conventional approach of using a heuristic constant threshold. It is found that original BIC penalty is too low and will not lead to a compact Decision Tree state tying model. Based on Wolfe's modification to the asymptotic null distribution, it is derived that two times BIC penalty should be used for Decision Tree state tying based on pBIC. Secondly, pBIC is studied as a model compression criterion for Decision Tree state tying based acoustic modeling. Experimental results on a large vocabulary (Wall STreet Journal) speech recognition task indicate that a compact Decision Tree could be achieved with almost no loss of the speech recognition performance.
Stjepan Picek - One of the best experts on this subject based on the ideXlab platform.
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Classification of Cancer Data: Analyzing Gene Expression Data Using a Fuzzy Decision Tree Algorithm
International Series in Operations Research & Management Science, 2017Co-Authors: Simone A. Ludwig, Stjepan Picek, Domagoj JakobovicAbstract:Decision Tree algorithms are very popular in the area of data mining since the algorithms have a simple inference mechanism and provide a comprehensible way to represent the model. Over the past years, fuzzy Decision Tree algorithms have been proposed in order to handle the uncertainty in the data. Fuzzy Decision Tree algorithms have shown to outperform classical Decision Tree algorithms. This chapter investigates a fuzzy Decision Tree algorithm applied to the classification of gene expression data. The fuzzy Decision Tree algorithm is compared to a classical Decision Tree algorithm as well as other well-known data mining algorithms commonly applied to classification tasks. Based on the five data sets analyzed, the fuzzy Decision Tree algorithm outperforms the classical Decision Tree algorithm. However, compared to other commonly used classification algorithms, both Decision Tree algorithms are competitive, but they do not reach the accuracy values of the best performing classifier. One of the advantages of Decision Tree models including the fuzzy Decision Tree algorithm is however the simplicity and comprehensibility of the model as demonstrated in the chapter.
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analyzing gene expression data fuzzy Decision Tree algorithm applied to the classification of cancer data
IEEE International Conference on Fuzzy Systems, 2015Co-Authors: Simone A. Ludwig, Domagoj Jakobovic, Stjepan PicekAbstract:In data mining, Decision Tree algorithms are very popular methodologies since the algorithms have a simple inference mechanism and provide a comprehensible way to represent the model in the form of a Decision Tree. Over the past years, fuzzy Decision Tree algorithms have been proposed in order to provide a way to handle uncertainty in the data collected. Fuzzy Decision Tree algorithms have shown to outperform classical Decision Tree algorithms. This paper investigates a fuzzy Decision Tree algorithm applied to the classification of gene expression data. The fuzzy Decision Tree algorithm is compared to a classical Decision Tree algorithm as well as other well-known data mining algorithms commonly applied to classification tasks. Based on the five data sets analyzed, the fuzzy Decision Tree algorithm outperforms the classical Decision Tree algorithm. However, compared to other commonly used classification algorithms, both Decision Tree algorithms are competitive, although both do not reach the accuracy values of the best performing classifier.
Rodrigo C Barros - One of the best experts on this subject based on the ideXlab platform.
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Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets
IEEE Transactions on Evolutionary Computation, 2014Co-Authors: Rodrigo C Barros, Marcio P Basgalupp, Alex A Freitas, A C P L F De CarvalhoAbstract:Decision-Tree induction algorithms are widely used in machine learning applications in which the goal is to extract knowledge from data and present it in a graphically intuitive way. The most successful strategy for inducing Decision Trees is the greedy top-down recursive approach, which has been continuously improved by researchers over the past 40 years. In this paper, we propose a paradigm shift in the research of Decision Trees: instead of proposing a new manually designed method for inducing Decision Trees, we propose automatically designing Decision-Tree induction algorithms tailored to a specific type of classification data set (or application domain). Following recent breakthroughs in the automatic design of machine learning algorithms, we propose a hyper-heuristic evolutionary algorithm called hyper-heuristic evolutionary algorithm for designing Decision-Tree algorithms (HEAD-DT) that evolves design components of top-down Decision-Tree induction algorithms. By the end of the evolution, we expect HEAD-DT to generate a new and possibly better Decision-Tree algorithm for a given application domain. We perform extensive experiments in 35 real-world microarray gene expression data sets to assess the performance of HEAD-DT, and compare it with very well known Decision-Tree algorithms such as C4.5, CART, and REPTree. Results show that HEAD-DT is capable of generating algorithms that significantly outperform the baseline manually designed Decision-Tree algorithms regarding predictive accuracy and F-measure.
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a hyper heuristic evolutionary algorithm for automatically designing Decision Tree algorithms
Genetic and Evolutionary Computation Conference, 2012Co-Authors: Rodrigo C Barros, Marcio P Basgalupp, A C P L F De Carvalho, Alex A FreitasAbstract:Decision Tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans. The most successful strategy for inducing Decision Trees, the greedy top-down approach, has been continuously improved by researchers over the years. This work, following recent breakthroughs in the automatic design of machine learning algorithms, proposes a hyper-heuristic evolutionary algorithm for automatically generating Decision-Tree induction algorithms, named HEAD-DT. We perform extensive experiments in 20 public data sets to assess the performance of HEAD-DT, and we compare it to traditional Decision-Tree algorithms such as C4.5 and CART. Results show that HEAD-DT can generate algorithms that significantly outperform C4.5 and CART regarding predictive accuracy and F-Measure.
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a survey of evolutionary algorithms for Decision Tree induction
Systems Man and Cybernetics, 2012Co-Authors: Rodrigo C Barros, Marcio P Basgalupp, A C P L F De Carvalho, Alex A FreitasAbstract:This paper presents a survey of evolutionary algorithms that are designed for Decision-Tree induction. In this context, most of the paper focuses on approaches that evolve Decision Trees as an alternate heuristics to the traditional top-down divide-and-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of Decision-Tree classifiers. The paper's original contributions are the following. First, it provides an up-to-date overview that is fully focused on evolutionary algorithms and Decision Trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy, which addresses works that evolve Decision Trees and works that design Decision-Tree components by the use of evolutionary algorithms. Finally, a number of references are provided that describe applications of evolutionary algorithms for Decision-Tree induction in different domains. At the end of this paper, we address some important issues and open questions that can be the subject of future research.
