- (Yang & Liu, 1999) ⇒ Yiming Yang, Xin Liu. (1999). “A Re-examination of Text Categorization Methods.” In: Proceedings of the ACM SIGIR Conference (SIGIR 1999). doi:10.1145/312624.312647
Subject Headings: Text Classification Algorithm, Empirical Algorithm Comparison Study.
- It reports experiments with five Text Classification Algorithms: SVM, kNN, NB, NNet, and LLSF.
- Its results suggest that SVM and kNN outperform the other Classifiers.
- Its results suggest NB underperforms all the other Classifiers.
- This paper reports a controlled study with statistical significance tests on five text categorization methods: the Support Vector Machines (SVM), a k-Nearest Neighbor (kNN) classifier, a neural network (NNet) approach, the Linear Least-squares Fit (LLSF) mapping and a Naive Bayes (NB) classifier. We focus on the robustness of these methods in dealing with a skewed category distribution, and their performance as function of the training-set category frequency. Our results show that SVM, kNN and LLSF significantly outperform NNet and NB when the number of positive training instances per category are small (less than ten), and that all the methods perform comparably when the categories are sufficiently common (over 300 instances).
- Automated text categorization (TC) is a supervised learning task, defined as assigning category labels (pre-defined) to new documents based on the likelihood suggested by a training set of labeled documents. It has raised open challenges for statistical learning methods, requiring empirical examination of their effectiveness in solving real-world problems which are often high-dimensional, and have a skewed category distribution over labeled documents. Topic spotting for newswire stories, for example, is one the most commonly investigated application domains in the TC literature. An increasing number of learning approaches have been applied, including regression models [9, 32], nearest neighbor classification [17, 29, 33, 31, 14], Bayesian probabilistic approaches [25, 16, 20, 13, 12, 18, 3], decision trees [9, 16, 20, 2, 12], inductive rule learning [1, 5, 6, 21], neural networks [28,22], on-line learning[6, 15] and Support Vector Machines .
- While the rich literature provides valuable information about individual methods, clear conclusions about cross-method comparison have been difficult because often the published results are not directly comparable.
- In this paper we presented a controlled study with significance analyses on five well-known text categorization methods. Our main conclusions are:
- Significance analyses can be applied to both a micro-level and macro-level evaluation of text categorization systems, and jointly used for cross-method comparison.
- The outcome of a significance test depends on the choice of performance measure, the sensitivity of the test, and the training-set frequency of categories being tested.
- For the micro-level performance on pooled category assignments, both a sign test and an error-based proportion test suggest that SVM and kNN signi cantly outperform the other classifiers, while NB significantly underperforms all the other classifiers.
- With respect to the macro-level (category-level) performance analysis using F1, all the significance tests we conducted suggest that SVM, kNN and LLSF belong to the same class, significantly outperforming NB and NNet.
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|1999 AReExaminationOfTextCategorizationMethods||Yiming Yang|
|A Re-examination of Text Categorization Methods||Proceedings of the ACM SIGIR Conference||http://nyc.lti.cs.cmu.edu/yiming/Publications/yang-sigir99.pdf||10.1145/312624.312647||1999|
Facts about "1999 AReExaminationOfTextCategorizationMethods"
|Author||Yiming Yang + and Xin Liu +|
|journal||Proceedings of the ACM SIGIR Conference +|
|title||A Re-examination of Text Categorization Methods +|