Search (2 results, page 1 of 1)

  • × author_ss:"Lee, G.G."
  • × theme_ss:"Automatisches Klassifizieren"
  1. Yoon, Y.; Lee, C.; Lee, G.G.: ¬An effective procedure for constructing a hierarchical text classification system (2006) 0.03 
    0.02894381 = product of:
  0.05788762 = sum of:
    0.05788762 = sum of:
      0.014207288 = weight(_text_:a in 5273) [ClassicSimilarity], result of:
        0.014207288 = score(doc=5273,freq=18.0), product of:
          0.053105544 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046056706 = queryNorm
          0.26752928 = fieldWeight in 5273, product of:
            4.2426405 = tf(freq=18.0), with freq of:
              18.0 = termFreq=18.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.0546875 = fieldNorm(doc=5273)
      0.043680333 = weight(_text_:22 in 5273) [ClassicSimilarity], result of:
        0.043680333 = score(doc=5273,freq=2.0), product of:
          0.16128273 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046056706 = queryNorm
          0.2708308 = fieldWeight in 5273, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0546875 = fieldNorm(doc=5273)
  0.5 = coord(1/2)
    Abstract
    In text categorization tasks, classification on some class hierarchies has better results than in cases without the hierarchy. Currently, because a large number of documents are divided into several subgroups in a hierarchy, we can appropriately use a hierarchical classification method. However, we have no systematic method to build a hierarchical classification system that performs well with large collections of practical data. In this article, we introduce a new evaluation scheme for internal node classifiers, which can be used effectively to develop a hierarchical classification system. We also show that our method for constructing the hierarchical classification system is very effective, especially for the task of constructing classifiers applied to hierarchy tree with a lot of levels.
    Date
    22. 7.2006 16:24:52
    Type
    a
  2. Yoon, Y.; Lee, G.G.: Efficient implementation of associative classifiers for document classification (2007) 0.00 
    0.0030444188 = product of:
  0.0060888375 = sum of:
    0.0060888375 = product of:
      0.012177675 = sum of:
        0.012177675 = weight(_text_:a in 909) [ClassicSimilarity], result of:
          0.012177675 = score(doc=909,freq=18.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.22931081 = fieldWeight in 909, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=909)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    In practical text classification tasks, the ability to interpret the classification result is as important as the ability to classify exactly. Associative classifiers have many favorable characteristics such as rapid training, good classification accuracy, and excellent interpretation. However, associative classifiers also have some obstacles to overcome when they are applied in the area of text classification. The target text collection generally has a very high dimension, thus the training process might take a very long time. We propose a feature selection based on the mutual information between the word and class variables to reduce the space dimension of the associative classifiers. In addition, the training process of the associative classifier produces a huge amount of classification rules, which makes the prediction with a new document ineffective. We resolve this by introducing a new efficient method for storing and pruning classification rules. This method can also be used when predicting a test document. Experimental results using the 20-newsgroups dataset show many benefits of the associative classification in both training and predicting when applied to a real world problem.
    Type
    a

Authors