The Experts below are selected from a list of 174126 Experts worldwide ranked by ideXlab platform
Liang Dao - One of the best experts on this subject based on the ideXlab platform.
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A New Multivariate Decision Tree Algorithm
Computer Science, 2008Co-Authors: Liang DaoAbstract:Decision Tree Algorithm in univariate tests caused large-scale,complex rules that are difficult to understand.Based on the rough sets theory of attributes reduction,the core of condition attributions and the weighted roughness of condition attributions,a new multivariate decision Tree Algorithm is proposed.A example shows in this paper,the decision Tree built by the method is more simple and has better classification result than that of ID3 Algorithm.
Liu Guo-hua - One of the best experts on this subject based on the ideXlab platform.
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Research on the Parallelism of Decision Tree Algorithm
Computer Engineering, 2002Co-Authors: Liu Guo-huaAbstract:It is needed to think about the problem with speed-up when data mining techniques are applied to database or data warehouse. If datasets mined are large over a special level, they can only be handled on supercomputers. The information mined from history datasets is invalid and even negative if the speed of mining system is too slow. As far as this point is concerned, this paper presents parallel principle of decision Tree Algorithm and discusses the performance of its parallelism.
Cheng Xiao-hui - One of the best experts on this subject based on the ideXlab platform.
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Parallelization of decision Tree Algorithm based on MapReduce
Journal of Computer Applications, 2012Co-Authors: Cheng Xiao-huiAbstract:In view of that the traditional decision Tree Algorithm that cannot solve the mass data mining and the multi-value bias problem of ID3 Algorithm,the paper designed and realized a parallel decision Tree classification Algorithm based on the MapReduce framework.This Algorithm adopted attribute similarity as the choice standard to avoid the multi-value bias problem of ID3 Algorithm,and used the MapReduce model to solve the mass data mining problems.According to the experiments on the Hadoop cluster set up by ordinary PCs,the decision Tree Algorithm based on MapReduce can deal with massive data classification.What's more,the Algorithm has good expansibility while ensuring the classification accuracy and can get close to linear speedup rate.
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.
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FUZZ-IEEE - Analyzing gene expression data: Fuzzy decision Tree Algorithm applied to the classification of cancer data
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 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.
Marco Renedo - One of the best experts on this subject based on the ideXlab platform.
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Probability Tree Algorithm for general diffusion processes.
Physical Review E, 2001Co-Authors: Lester Ingber, Colleen Chen, Radu Paul Mondescu, David Muzzall, Marco RenedoAbstract:Motivated by path-integral numerical solutions of diffusion processes, PATHINT, we present a Tree Algorithm, PATHTree, which permits extremely fast accurate computation of probability distributions of a large class of general nonlinear diffusion processes.
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Probability Tree Algorithm for General Diffusion Processes
SSRN Electronic Journal, 2001Co-Authors: Lester Ingber, Colleen Chen, Radu Paul Mondescu, David Muzzall, Marco RenedoAbstract:Motivated by path-integral numerical solutions of diffusion processes, PATHINT, we present a new Tree Algorithm, PATHTree, which permits extremely fast accurate computation of probability distributions of a large class of general nonlinear diffusion processes.
