R和WEKA上的支持向量机

My data generated strange results with svm on R from the e1071 package, so I tried to check if the R svm can generate same result as WEKA (or python), since I've been using WEKA in the past.I googled the question and found one that has the exact same confusion with me but without an answer. This is the question.So I hope that I could get an answer here.To make things easier, I'm also using the iris data set, and train a model (SMO in WEKA, and svm from R package e1071) using the whole iris data, and test on itself.WEKA parameters:weka.classifiers.functions.SMO -C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V 10 -W 1 -K "weka.classifiers.functions.supportVector.RBFKernel -G 0.01 -C 250007"Other than default, I changed kernel into RBFKernel to make it consistant with the R fucntion.The result is: a b c <-- classified as 50 0 0 | a = Iris-setosa 0 46 4 | b = Iris-versicolor 0 7 43 | c = Iris-virginicaR script:library(e1071)model <- svm(iris[,-5], iris[,5], kernel="radial", epsilon=1.0E-12)res <- predict(model, iris[,-5])table(pred = res, true = iris[,ncol(iris)])The result is: truepred setosa versicolor virginica setosa 50 0 0 versicolor 0 48 2 virginica 0 2 48I'm not a machine learning person, so I'm guessing the default parameters are very different for these two methods. For example, e1071 has 0.01 as default epsilon and WEKA has 1.0E-12. I tried to read through the manuals and wanted to make all parameters identical, but a lot of parameters do not seem comparable to me.Thanks. 解决方案 Refer to http://weka.sourceforge.net/doc.dev/weka/classifiers/functions/SMO.html for the RWeka parameters for SMO and use ?svm to find the corresponding parameters for e1071 svm implementation.As per ?svm, R e1071 svm is an interface to libsvm and seems to use standard QP solvers. For multiclass-classification with k levels, k>2, libsvm uses the ‘one-against-one’-approach, in which k(k-1)/2 binary classifiers are trained; the appropriate class is found by a voting scheme. libsvm internally uses a sparse data representation, which is also high-level supported by the package SparseM.To the contrary ?SMO in RWeka implements John C. Platt's sequential minimal optimization algorithm for training a support vector classifier using polynomial or RBF kernels. Multi-class problems are solved using pairwise classification.So, these two implementations are different in general (so the results may be a little different). Still if we choose the corresponding hyper-parameters same, the confusion matrix is almost the same:library(RWeka)model.smo <- SMO(Species ~ ., data = iris,control = Weka_control(K = list("RBFKernel", G=2), C=1.0, L=0.001, P=1.0E-12, N=0, V=10, W=1234))res.smo <- predict(model.smo, iris[,-5])table(pred = res.smo, true = iris[,ncol(iris)]) truepred setosa versicolor virginica setosa 50 0 0 versicolor 0 47 1 virginica 0 3 49library(e1071)set.seed(1234)model.svm <- svm(iris[,-5], iris[,5], kernel="radial", cost=1.0, tolerance=0.001, epsilon=1.0E-12, scale=TRUE, cross=10)res.svm <- predict(model.svm, iris[,-5])table(pred = res.svm, true = iris[,ncol(iris)]) truepred setosa versicolor virginica setosa 50 0 0 versicolor 0 49 1 virginica 0 1 49Also refer to this: [https://stats.stackexchange.com/questions/130293/svm-and-smo-main-differences][1] and this [https://www.quora.com/Whats-the-difference-between-LibSVM-and-LibLinear][1] 这篇关于R和WEKA上的支持向量机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！