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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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The matlab code implements the ensemble of decision tree classifiers proposed in: "L. Nanni and A. Lumini, Input Decimated Ensemble based on Neighborhood Preserving Embedding for spectrogram classification, Expert Systems With Applications doi:10.101
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MATLAB cross-validation tool for classification and regression v0.1
FEATURES:
+ K-fold cross validation.
+ Arbitrary train and prediction functions with parameters can be used.
+ Arbitrary loss function can be used.
+ Wrappers for
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matlab实现决策树C4.5算法,首先利用训练数据创建决策树，再用测试数据对决策树进行剪枝。-C4.5 decision tree algorithm matlab realize, first use training data to create decision trees, and then test data for decision tree pruning.
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如何使用matlab自带的决策树进行数据的分类，包括分类树，回归树-How to use matlab own decision tree classification of data, including classification trees, regression trees
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决策树的Matlab实现，实现了分类问题和回归问题，有很好的调试结果-Matlab implementation of decision trees
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决策树，实例：matlab实现使用决策树顶预测隐形眼镜类型。-Splitting datasets one feature at a time: decision trees
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使用matlab建立决策树，分析数据。决策过程中使用了matlab自带的建立决策树的函数。-Using decision trees to predict contact lens type.Training data file (lense.txt) and descr iption file (comments.txt).
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要说随机森林，必须先讲决策树。决策树是一种基本的分类器，一般是将特征分为两类（决策树也可以用来回归，不过本文中暂且不表）。构建好的决策树呈树形结构，可以认为是if-then规则的集合，主要优点是模型具有可读性，分类速度快。(In machine learning, a random forest is a classifier that contains multiple decision trees, and its output category is determined by the m
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