R -在DTM的基础上测试DTM 1.这样我就可以预测dtm1的类别

Question

库函数

     library(tm)
     library(e1071)
     library(plyr)

插入要分类的期刊名称

sample = c(
    "An Inductive Inference Machine",
    "Computing Machinery and Intelligence",
    "On the translation of languages from left to right",
    "First Draft of a Report on the EDVAC",
    "The Rendering Equation")
corpus <- Corpus(VectorSource(sample))
corpus <- tm_map(corpus, removeNumbers)
corpus <- tm_map(corpus, removePunctuation)
corpus <- tm_map(corpus, tolower)
corpus <- tm_map(corpus, removeWords, stopwords("english"))
corpus <- tm_map(corpus, stemDocument,language="english")
corpus <- tm_map(corpus, stripWhitespace)
dtm <- DocumentTermMatrix(corpus)

术语文档矩阵作为训练集

inspect(dtm)
Category=c("Machine learning","Artificial intelligence","Compilers","Computer   architecture","Computer graphics")

类别的声明

my.data=data.frame(as.matrix(dtm),Category)
my.data 
sample = c(
    "gprof: A Call Graph Execution Profiler",
    "Architecture of the IBM System/360",
    "A Case for Redundant Arrays of Inexpensive Disks (RAID)",
    "Determining Optical Flow",
    "A relational model for large shared data banks",
    "some complementarity problems of z and lyoponov like transformations on       edclidean  jordan algebra")
corpus <- Corpus(VectorSource(sample))
corpus <- tm_map(corpus, removeNumbers)
corpus <- tm_map(corpus, removePunctuation)
corpus <- tm_map(corpus, tolower)
corpus <- tm_map(corpus, removeWords, stopwords("english"))
corpus <- tm_map(corpus, stemDocument,language="english")
corpus <- tm_map(corpus, stripWhitespace)
dtm1 <- DocumentTermMatrix(corpus)

术语文档矩阵作为测试集

inspect(dtm1) 

Answer 1

好吧，您的样本数据绝对没有重叠的术语，因此您在那里没有太多可以做的。tm库不为单词分配意义，它只是测量它们的相关性。所以你需要提供足够的重叠数据，这样它才有机会将新的输入与现有的语料库相匹配。

一旦你有了真实的数据，你就有很多选择来构建一个模型。你可以使用像class包中的kNN分类器，或者像rpart包中的决策树，或者像nnet包中的神经网络。在这个演讲中，每一种都有例子。但这取决于你决定什么对你的数据是正确的。这部分不是编程相关的问题