Description: 数据挖掘中的决策树C4.5算法的实现,用matlab实现-Data Mining Decision Tree Algorithm of C4.5, using Matlab to achieve Platform: |
Size: 2308 |
Author:利军 |
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Description: 数据挖掘中的决策树C4.5算法的实现,用matlab实现-Data Mining Decision Tree Algorithm of C4.5, using Matlab to achieve Platform: |
Size: 2048 |
Author:利军 |
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Description: C4.5算法有如下优点：产生的分类规则易于理解，准确率较高。其缺点是：在构造树的过程中，需要对数据集进行多次的顺序扫描和排序，因而导致算法的低效。此外，C4.5只适合于能够驻留于内存的数据集，当训练集大得无法在内存容纳时程序无法运行。-C4.5 algorithm has the following advantages: the classification rules easier to understand, accurate and a higher rate. Its shortcomings are as follows: in the tree structure, the need for a number of data sets the order of scanning and sorting, thus leading to inefficient algorithms. In addition, C4.5 can only be applied to the presence of a data set in memory, when the training set too great to accommodate in memory when the program can not run. Platform: |
Size: 1024 |
Author:xinyuanwo |
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Description: C5.0 决策树源码, 此算法要优于C4.5算法-C5.0 decision tree source, this algorithm is superior to C4.5 algorithm Platform: |
Size: 81920 |
Author:Jianfei Wu |
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Description: NeC4.5 is a variant of C4.5 decision tree, which could generate decision trees more accurate than standard C4.5 decision trees, through regarding a neural network ensemble as a pre-process of C4.5 decision tree.
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Size: 8192 |
Author: |
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Description: c4.5 关于决策树decision tree的matlab实现程序 -c4.5 decision tree decision tree on the realization of the matlab program Platform: |
Size: 4096 |
Author:凌风 |
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Description: his algorithm was proposed by Quinlan (1993). The C4.5 algorithm generates a classification-decision tree for the given data-set by recursive partitioning of data. The decision is grown using Depth-first strategy. The algorithm considers all the possible tests that can split the data set and selects a test that gives the best information gain. For each discrete attribute, one test with outcomes as many as the number of distinct values of the attribute is considered. For each continuous attribute, binary tests involving every distinct values of the attribute are considered. In order to gather the entropy gain of all these binary tests efficiently, the training data set belonging to the node in consideration is sorted for the values of the continuous attribute and the entropy gains of the binary cut based on each distinct values are calculated in one scan of the sorted data. This process is repeated for each continuous attributes. Platform: |
Size: 2048 |
Author:rajesh |
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Description: 该源代码主要实现C4.5决策树算法，C4.5是对ID3算法的一种改进，它完善了ID3算法，补充了其算法的一点不足-The source code is the main achievement of C4.5 decision tree algorithm, C4.5 is an improved ID3 algorithm, which improved the ID3 algorithm, the algorithm added a little less than its Platform: |
Size: 15360 |
Author:小强 |
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Description: This file contains matlab code for c4.5 decision tree code, which is used to study id3 algorithm based machine learning code Platform: |
Size: 2048 |
Author:sgnaren |
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Description: 使用matlab实现C4.5决策树算法核心(Implementing the Core of C4.5 Decision Tree Algorithms with MATLAB) Platform: |
Size: 2048 |
Author:lbt136136 |
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Search - C4.5 decision tree MATLAB