二进制分类器在libsvm中给出错误的结果

本文介绍了二进制分类器在libsvm中给出错误的结果的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述 29岁程序员，3月因学历无情被辞！ 我有一个584乘100的数据集，每个数据有584个特征向量（总共100个训练向量）。我用Java实现了Libsvm。 （（1）.trainX大小为584 x 100，（2）.biny是第1类为+1，第2类为-1的数组，（3）.LinearSVMNormVector是模型的结果w（权重向量） ）。下面是我的代码 - < pre lang = java> // 0到1之间的比例训练数据 double [] [] trainX_scale = new double [trainX.length] [trainX [ 0 ]。length]; for （ int i = 0 ; i< trainX.length; i ++）{ double min = Double.MAX_VALUE; double max = Double.MIN_VALUE; for （ int inner = 0 ; inner< trainX [i] .length; inner ++）{ if （trainX [i] [inner]< min） min = trainX [i] [inner]; if （trainX [i] [inner]> max） max = trainX [i] [inner]; } double difference = max - min; for （ int inner = 0 ; inner< trainX [i] .length; inner ++）{ trainX_scale [i] [inner] =（trainX [i] [inner] - min）/ difference; } } // 准备svm节点 svm_node [] [] SVM_node_Train = new svm_node [trainX [ 0 ]。 ] [trainX.length]; for （ int p = 0 ; p< trainX [ 0 ] .length; p ++）{ for （ int q = 0 ; q< trainX.length; q ++）{ SVM_node_Train [p] [q] = new svm_node（）; SVM_node_Train [p] [q] .index = q; SVM_node_Train [p] [q] .value = trainX_scale [q] [p]; } } double [] biny_SVM = new double [biny.length]; // for svm compatible for （ int p = 0 ; p< biny.length; p ++）{ biny_SVM [p] = biny [p]; } svm_problem SVM_Prob = new svm_problem（）; SVM_Prob.l = trainX [ 0 ]。length; SVM_Prob.x = SVM_node_Train; SVM_Prob.y = biny_SVM; svm_parameter SVM_Param = new svm_parameter（）; SVM_Param.svm_type = 0 ; SVM_Param.kernel_type = 2 ; SVM_Param.cache_size = 100 ; SVM_Param.eps = 0 。 0000001 ; SVM_Param.C = 1 。 0 ; SVM_Param.gamma = 0 。 5 ; svm_model SVM_Model = new svm_model（）; SVM_Model.param = SVM_Param; SVM_Model.l = trainX [ 0 ]。length; SVM_Model.nr_class = 2 ; SVM_Model.SV = SVM_node_Train; // SVM_Model.label = biny; // String check = svm.svm_check_parameter（SVM_Prob，SVM_Param）; // // System.out.println（check）; double [] target = new double [biny.length]; // for svm compatible Arrays .fill（target， 0 。 0 ）; svm.svm_cross_validation（SVM_Prob，SVM_Param， 2 ，target）; // 训练分类器 svm_model test_model = svm。 svm_train（SVM_Prob，SVM_Param）; / * *********获取libsvm *的培训结果********* / // double [ ] [] weights1 = test_model.sv_coef; double 偏差= test_model.rho [ 0 ]; double NumberOfSupportVectors = svm.svm_get_nr_sv（test_model）; double [] SupportVectorIDs = new INT [NumberOfSupportVectors]; svm.svm_get_sv_indices（test_model，SupportVectorIDs）; svm_node [] [] SV = test_model.SV; double [] [] SupportVectors = new double [SV.length] [SV [ 0 ] .length]; for （ int ii = 0; ii< SV.length; ii ++）{ for （ int jj = 0; jj< SV [ 0 ]。长度; jj ++）{ SupportVectors [ii] [jj] = SV [ii] [jj] .value; } } double [] SupportVectorWeights = test_model.sv_coef [ 0 ]; double [] LinearSVMNormVector = new double [SupportVectors [ 0 ]。length]; for （ int ii = 0; ii< msvm [ 0 ]。SupportVectors [ 0 ] .length; ii ++）{ for （ int jj = 0; jj< SupportVectors.length; jj ++）{ LinearSVMNormVector [ii] + =（SupportVectors [jj] [ii] * SupportVectorWeights [jj]）; } } 我的测试数据上的这个型号我得到的超过90 ％错误分类。我有点困惑。有人可以告诉我，如果分类器设置有什么问题吗？ 谢谢！解决方案 你是如何获得C和gamma值的？ 尝试使用网格搜索方法找到它 http://scikit-learn.org/stable/modules/grid_search .html 结果可能会有所改善 还可以尝试像weka这样的工具来验证功能是否足够好 http://www.cs.waikato.ac.nz/ml/weka/ 这是我的理解 每个特征代表一行，每列代表训练样本的实例 584行和100列 如果trainX [0] .length = 100且trainX.length = 584 然后节点看起来没问题 svm模型看起来像 带有rbf内核的CSVM看起来不错 I have a 584 by 100 data set with each data having 584 feature vectors(total 100 training vectors). I have implemented Libsvm in Java. ((1). trainX size is 584 x 100, (2). biny is the array which has +1 for class one and -1 for class 2, (3). LinearSVMNormVector is the resultant w (weight vector) of the model). Below is my code -<pre lang="java">// scale train data between 0 and 1 double[][] trainX_scale = new double[trainX.length][trainX[0].length]; for (int i = 0; i < trainX.length; i++) { double min = Double.MAX_VALUE; double max = Double.MIN_VALUE; for (int inner = 0; inner < trainX[i].length; inner++) { if (trainX[i][inner] < min) min = trainX[i][inner]; if (trainX[i][inner] > max) max = trainX[i][inner]; } double difference = max - min; for (int inner = 0; inner < trainX[i].length; inner++) { trainX_scale[i][inner] = (trainX[i][inner] - min)/ difference; } } // prepare the svm node svm_node[][] SVM_node_Train = new svm_node[trainX[0].length][trainX.length]; for (int p = 0; p < trainX[0].length; p++) { for (int q = 0; q < trainX.length; q++) { SVM_node_Train[p][q] = new svm_node(); SVM_node_Train[p][q].index = q; SVM_node_Train[p][q].value = trainX_scale[q][p]; } } double[] biny_SVM = new double[biny.length];// for svm compatible for (int p = 0; p < biny.length; p++) { biny_SVM[p] = biny[p]; } svm_problem SVM_Prob = new svm_problem(); SVM_Prob.l = trainX[0].length; SVM_Prob.x = SVM_node_Train; SVM_Prob.y = biny_SVM; svm_parameter SVM_Param = new svm_parameter(); SVM_Param.svm_type = 0; SVM_Param.kernel_type = 2; SVM_Param.cache_size = 100; SVM_Param.eps = 0.0000001; SVM_Param.C = 1.0; SVM_Param.gamma = 0.5; svm_model SVM_Model = new svm_model(); SVM_Model.param = SVM_Param; SVM_Model.l = trainX[0].length; SVM_Model.nr_class = 2; SVM_Model.SV = SVM_node_Train; //SVM_Model.label = biny; // String check =svm.svm_check_parameter(SVM_Prob, SVM_Param); // // System.out.println(check); double[] target = new double[biny.length];// for svm compatible Arrays.fill(target, 0.0); svm.svm_cross_validation(SVM_Prob, SVM_Param, 2, target); // train the classifier svm_model test_model = svm.svm_train(SVM_Prob, SVM_Param); /********** get the training results of libsvm **********/ //double[][] weights1 = test_model.sv_coef; double Bias = test_model.rho[0]; double NumberOfSupportVectors = svm.svm_get_nr_sv(test_model); double [] SupportVectorIDs = new int[NumberOfSupportVectors]; svm.svm_get_sv_indices(test_model, SupportVectorIDs); svm_node[][] SV= test_model.SV; double [][]SupportVectors=new double [SV.length][SV[0].length]; for(int ii=0;ii<SV.length;ii++){ for(int jj=0;jj<SV[0].length;jj++){ SupportVectors[ii][jj]=SV[ii][jj].value; } } double [] SupportVectorWeights=test_model.sv_coef[0]; double[] LinearSVMNormVector = new double [SupportVectors[0].length]; for (int ii=0;ii<msvm[0].SupportVectors[0].length;ii++){ for (int jj=0;jj<SupportVectors.length;jj++){ LinearSVMNormVector[ii] += (SupportVectors[jj][ii] * SupportVectorWeights[jj]); } }with this model on my test data I am getting more than 90% mis-classification. I am a little confused. Can someone please tell me if there is anything wrong in the classifier set up?Thanks! 解决方案 How did you obtain the C and gamma valuestry using a grid search approach to find ithttp://scikit-learn.org/stable/modules/grid_search.htmlThe result might improveAlso try a tool like weka to verify if the features are good enoughhttp://www.cs.waikato.ac.nz/ml/weka/This is my understandingEach feature represents a row and each column an instance of the training samples584 rows and 100 columnsIf trainX[0].length = 100 and trainX.length = 584then nodes looks okThe svm model looks likeA CSVM with rbf kernel which looks ok 这篇关于二进制分类器在libsvm中给出错误的结果的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！ 上岸，阿里云！