WekaAdaboost源代码分析.doc

Weka[14] Adaboost 源代码分析 作者：Koala++/屈伟 需要注意的是 AdaBoostM1 继承自 RandomizableIteratedSingleClassifierEnhancer，天哪， 这么长的名字，他们也起的出来。 这个要看一下构造函数： public AdaBoostM1() { m_Classifier = new weka.classifiers.trees.DecisionStump(); } 这里的 m_Classifier 是继承自曾祖父 SingleClassifierEnhancer 的，DecisionStump 就是只 有一个结点的决策树，stump 是树桩的意思，蛮形象的。 public void buildClassifier(Instances data) throws Exception { super.buildClassifier(data); // can classifier handle the data? getCapabilities().testWithFail(data); // remove instances with missing class data = new Instances(data); data.deleteWithMissingClass(); // only class? - build ZeroR model if (data.numAttributes() == 1) { System.err .println(Cannot build model (only class attribute present in data!), + using ZeroR model instead!); m_ZeroR = new weka.classifiers.rules.ZeroR(); m_ZeroR.buildClassifier(data); return; } else { m_ZeroR = null; } m_NumClasses = data.numClasses(); if ((!m_UseResampling) (m_Classifier instanceof WeightedInstancesHandler)) { buildClassifierWithWeights(data); } else { buildClassifierUsingResampling(data); } } 这里 buildClassifier 就是复制 m_NumIterations 个分类器，具体的代码如下： public void buildClassifier(Instances data) throws Exception { if (m_Classifier == null) { throw new Exception(A base classifier has not been specified!); } m_Classifiers = Classifier.makeCopies(m_Classifier, m_NumIterations); } 如果就只有 1 个属性，那么表示只有类别属性，用 m_ZeroR 来分类就可以了，ZeroR 分 类是返回类别数最多的那个类别。再向下，判断是否使用重新采样，分类器是否可以考虑带 权样本。先看一下 buildClassifierWithWeights(删除了前面的部分代码)： protected void buildClassifierWithWeights(Instances data) throws Exception { // Do boostrap iterations for (m_NumIterationsPerformed = 0; m_NumIterationsPerformed m_Classifiers.length; m_NumIterationsPerformed++) { // Select instances to train the classifier on if (m_WeightThreshold 100) { trainData = selectWeightQuantile(training, (double) m_WeightThreshold / 100); } else { trainData = new Instances(training, 0, numInstances); } // Build the classifier if (m_Classifiers[m_NumIterationsPerformed] i