文字分类NaiveBayes [英] Text classification NaiveBayes
问题描述
我正在尝试按类别对一系列文本示例新闻进行分类。我在数据库中有庞大的新闻文本数据集,其中包含类别。应该训练机器并确定新闻类别。
I am trying to classify a series of text example News by category. I have huge dataset of news text with category in database. Machine should be trained and decide the news category.
public static string[] Tokenize(string text)
{
StringBuilder sb = new StringBuilder(text);
char[] invalid = "!-;':'\",.?\n\r\t".ToCharArray();
for (int i = 0; i < invalid.Length; i++)
sb.Replace(invalid[i], ' ');
return sb.ToString().Split(new[] { ' ' }, System.StringSplitOptions.RemoveEmptyEntries);
}
private void Form1_Load(object sender, EventArgs e)
{
string strDSN = "Provider=Microsoft.ACE.OLEDB.12.0;Data Source = c:\\users\\158820\\Documents\\Database4.accdb";
string strSQL = "SELECT * FROM NewsRepository";
// create Objects of ADOConnection and ADOCommand
OleDbConnection myConn = new OleDbConnection(strDSN);
OleDbDataAdapter myCmd = new OleDbDataAdapter(strSQL, myConn);
myConn.Open();
DataSet dtSet = new DataSet();
myCmd.Fill(dtSet, "NewsRepository");
DataTable dTable = dtSet.Tables[0];
myConn.Close();
StringBuilder sWords = new StringBuilder();
string[][] swords = new string[dTable.Rows.Count][];
int i = 0;
foreach (DataRowView dr in dTable.DefaultView)
{
swords[i] = Tokenize(dr[1].ToString());
i++;
}
Codification codebook = new Codification(dTable, new string[] { "NewsTitle", "Category" });
DataTable symbols = codebook.Apply(dTable);
int[][] inputs = symbols.ToJagged<int>(new string[] { "NewsTitle" });
int[] outputs = symbols.ToArray<int>("Category");
bagOfWords(inputs, outputs);
}
private static void bagOfWords(int[][] inputs, int[] outputs)
{
var bow = new BagOfWords<int>();
var quantizer = bow.Learn(inputs);
string filenamebow = Path.Combine(Application.StartupPath, "News_BOW.accord");
Serializer.Save(obj: bow, path: filenamebow);
double[][] histograms = quantizer.Transform(inputs);
// One way to perform sequence classification with an SVM is to use
// a kernel defined over sequences, such as DynamicTimeWarping.
// Create the multi-class learning algorithm as one-vs-one with DTW:
var teacher = new MulticlassSupportVectorLearning<ChiSquare, double[]>()
{
Learner = (p) => new SequentialMinimalOptimization<ChiSquare, double[]>()
{
// Complexity = 100 // Create a hard SVM
}
};
// Learn a multi-label SVM using the teacher
var svm = teacher.Learn(histograms, outputs);
// Get the predictions for the inputs
int[] predicted = svm.Decide(histograms);
// Create a confusion matrix to check the quality of the predictions:
var cm = new GeneralConfusionMatrix(predicted: predicted, expected: outputs);
// Check the accuracy measure:
double accuracy = cm.Accuracy;
string filename = Path.Combine(Application.StartupPath, "News_SVM.accord");
Serializer.Save(obj: svm, path: filename);
}
我对如何训练Accord.net对象有点困惑。我能够序列化经过训练的模型(9个类别中的3600个独特新闻大约需要106 MB)
I am bit confused on how to train accord.net objects. I am able to serialize the trained model (which is approx 106 MB for 3600 unique news within in 9 categories)
我如何使用该模型来预测新的新闻文本集?
How do I use the model to predict the category of a new set of news text?
推荐答案
对不在训练集中的数据使用模型就像调用svm一样简单另一个决定:
Using your model on data not in your training set is as simple as calling your svm to make another decision:
svm.Decide(outofSampleData)
由于已经序列化了训练好的模型,因此可以使用 Serializer.Load< T>
实例化svm对象,该文档记录了此处。
Since you have serialized your trained model you can instantiate the svm object using Serializer.Load<T>
which is documented here.
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